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Full text of "Essays in cross-country economic growth"

ESSAYS IN CROSS-COUNTRY ECONOMIC GROWTH 



BY 
HAMID-REZA BARADARAN-SHORAKA 



A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL 

OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT 

OF THE REQUIREMENTS FOR THE DEGREE OF 

DOCTOR OF PHILOSOPHY 



UNIVERSITY OF FLORIDA 
1992 



ACKNOWLEDGEMENTS 
This dissertation could not have been successfully 
completed without the support and guidance of Professor James 
D. Adams. I wish to thank him for his useful comments, 
insights and faith in me which kept me on track whenever I was 
faltering. I would also like to thank Professors Mark Rush and 
Prakash Loungani for their invaluable comments and 
suggestions. I am grateful also to Professors David Denslow 
and James Seale for serving on my dissertation committee and 
for their help. I am thankful also to Professors Lawrence 
Kenny and William Bomberger for their comments. 

I also appreciate the support of the Division of 
Sponsored Research of the University of Florida in my final 
year of study. 

Finally, I could not have endured the Ph.D. program 
without continual support from my family. Without the patience 
and understanding of my wife Mansoureh, I could not have 
either started or successfully concluded my doctorate. I thank 
my own and my wife's parents for all the support they have 
given me over the years. I also thank my brothers-in-law Ali- 
Reza, Mohammad-Reza, and All for their encouragement 
throughout my education. Last, the presence of my lovely sons 
Majid, Mohammad, and Massoud kept me going during these years. 

ii 



TABLE OF CONTENTS 

ACKNOWLEDGEMENTS ii 

ABSTRACT iv 

CHAPTERS 

1 INTRODUCTION x 

2 OIL PRICE CHANGES AND GNP FLUCTUATIONS 7 

Introduction 7 

Trends in Macro Activities !!!!!!!!! 10 

Consumption of Oil by Different Sectors: OECD Evidence 11 

Model and Methodology 12 

Empirical Results ! ! ! ! 17 

Conclusion 20 

3 HUMAN CAPITAL AND GOVERNMENT POLICIES 30 

Introduction 30 

Data and Expected Results 34 

Empirical Results .!!..!. 37 

Interpretation of the Findings 49 

Conclusion 52 

Notes 61 

4 TECHNOLOGY CREATION AND TECHNOLOGY TRANSFER 63 

Introduction 63 

Description of the Data 68 

Empirical Results .'."!!!.".'."...'." 73 

Interpretation of the Findings !."!.*.*.".*.*!.*.*!!! 84 

Conclusion ' ' 85 

Notes [ g6 

5 SUMMARY AND CONCLUSIONS 97 

APPENDICES 

A DEFINITIONS OF VARIABLES IN CHAPTER 2 . 102 

B DEFINITIONS AND METHOD OF CALCULATION OF MEAN ' YEARS ' OF 
SCHOOLING IN CHAPTER 3 T . 104 

REFERENCES 

BIOGRAPHICAL SKETCH ....'!.*!.'!!.'!.'.".*." .' ." .' .* .' .* [ ] ' ] .' ] .' ] .' ] [ '/// JJ® 

iii 



Abstract of Dissertation Presented to the Graduate School 

of the University of Florida in Partial Fulfillment of the 

Requirements for the Degree of Doctor of Philosophy 



ESSAYS IN CROSS-COUNTRY ECONOMIC GROWTH 

By 

Hamid-Reza Baradaran-Shoraka 

August 1992 

Chairman: Professor James D. Adams 
Major Department: Economics 

This dissertation consists of three essays that explore 

a variety of factors affecting economic growth across 

countries. 

The first essay deals with fluctuations in oil prices. 
These have trend effect as well as cyclical effects on growth. 
Data for ten OECD countries shows that the relative share of 
a country's transportation sector has a major impact on that 
nation's petroleum dependency. More important, I find that the 
transport sector's oil price elasticity is a key determinant 
of growth. 

The second essay analyzes why countries grow at different 
rates from an investment perspective. From my empirical work 
it appears that nations with higher initial incomes grow more 
slowly than countries with initially lower incomes. This might 

iv 






seem to imply income convergence. However, I find that the 
initial stock of human capital and its subsequent growth are 
associated with faster growth which somewhat attenuates 
convergence. As in recent growth models, I find that 
government consumption, political instability, and higher 
rates of population growth are associated with slower growth. 
The third essay studies technology's contribution to 
growth. In advanced economies, consistent with their 
comparative advantage in R&D, domestic technology proxied by 
resident patents strongly influences growth. However, my 
results suggest that imitation by less advanced countries of 
technologies developed in those more advanced is the principal 
engine of growth in these nations. I also find that in 
advanced countries, inventions per scientist and engineer 
raise the rates of growth which is consistent with the 
literature on invention exhaustion. Finally, I find that the 
potential for enhanced division of labor through interrelated 
growth of advanced and follower countries, is associated with 
higher growth in the advanced countries, but that technology 
acquisition in the newly industrializing countries is 
associated with deceleration of growth in the advanced 
countries. 



CHAPTER 1 
INTRODUCTION 

The substance of this dissertation is contained in three 
independent but related essays comprising chapters 2, 3, and 
4. All deal different factors impinging on economic growth 
across countries. 

This dissertation considers one determinant of 
cyclicality of growth, oil price shocks, and two key 
determinants of long-term growth: the accumulation of capital 
and improvements in technology. Capital here broadly 
encompasses physical capital (machines and buildings) , as well 
as human capital (improvements in the guality of labor force 
due to education and training) . We shall see that the 
accumulation of capital is an important element of growth. We 
shall also find that technological advance plays a crucial 
role in sustaining growth in the long run. In addition, the 
dissertation studies the impact of oil price shocks on growth. 
The data that I use on growth rates of real per capita 
income in 114 countries for the period I960 to 1985 is based 
on Summers and Heston (1988). As a result of their work, two 
facts about world economic growth have emerged. First, growth 
in per capita income has occurred continually in many 
countries over long periods of time. Second, this performance 






2 
has varied enormously across countries and over time. Also, 
Maddison (1982) looked over still longer periods and finds the 
same sustained growth. 

How do the above facts, sustained positive growth rates 
that vary systematically from country to country, bear on 
theorists' attempts to explain the process of economic growth? 
Classical writers like Mill and Marx speculated that standards 
of living could not rise indefinitely unless advances in 
technology helped augment the productivity of resources. This 
proposition received support from neoclassical growth 
theorists, who built models based solely on capital 
accumulation. In the latter models production of output was 
characterized by diminishing returns to capital and a steady 
state ratio of capital per head. This suggests that net 
investment per capita goes to zero in the absence of 
technological progress. But the fact that investment has 
continued for more than two centuries since the industrial 
revolution implies that technical change has played a crucial 
role in the growth process. 

In the more recent literature, Paul Romer (1986, 1990) 
stresses the role played by the accumulation of disembodied 
knowledge, as opposed to human capital. In his 1986 paper, he 
proposes a model in which growth takes place because the 
production function is subject to increasing returns to scale 
through knowledge spillovers. In his later paper, Romer 
explicitly allows for a permanent effect on the rate of growth 



3 
stemming from the stock of initial human capital. His model 
suggests an empirical "fanning out" of incomes among 
countries: very low levels of human capital result in very 
slow growth in underdeveloped economies; very high levels of 
human capital cause very rapid growth in developed economies. 
The same reasoning suggests that pooling of human capital 
would raise world growth rates. 

Lucas (1988) takes a different approach to the role of 
human capital in growth models. He postulates both an internal 
and external effect of human capital. The internal effect is 
the impact of an individual ' s human capital on his private 
marginal product. The external effect, which is identified 
with the country's average level of human capital, contributes 
to the productivity of all persons and factors of production. 
This model implies strictly parallel growth paths: economies 
that are initially poor will remain relatively poor, though 
their long-run rate of income growth will be the same as that 
of initially and permanently wealthier economies. 

Both Romer's (1990) fanning out results and Lucas's 

(1988) parallel growth path results ignore technology 

transfer, which can result in convergence, if follower 

countries are "activist" enough in their education and 

technology policies. 

Evenson (1984), basing his analysis on data on patented 
inventions from many countries, reaches two principal 
conclusions: first, the data show comparative advantage 



4 
patterns in invention similar to patterns observed in 
production. The production of pioneering invention is 
concentrated in certain firms located in countries with the 
best laboratories. Large parts of industry in most countries 
import inventions and concentrate on adaptive invention rather 
than investing heavily in R&D. Second, the data show that 
inventions per scientist and engineer have declined from the 
late 1960s to 1970s in almost all of the 50 countries for 
which data are available, which may suggest diminishing 
returns to invention in the present era. 

Grossman and Helpman (1991) make much of the fact that 
countries vary greatly in their growth performances. They 
point out that a reading of recent economic history suggests 
two important trends. First, technological innovations are 
ever more important contributors to growth. Second, nations 
are becoming increasingly open and interdependent. The two are 
not unrelated. Grossman and Helpman suggest that rapid 
communication and close contacts among innovators in different 
countries facilitate the process of invention and the spread 
of new ideas. 

Finally, Krugman (1979) in his famous model of product 
cycle divided countries into innovating North and 
noninnovating South. Innovation consists of the development of 
new products, which can be produced at first only in the 
North, but eventually the technology of production is 
transferred to the South. This technological lag gives rise to 



5 
trade, with the North exporting new products and importing old 
products . 

The dissertation is built on these themes. The first 
essay in the dissertation focuses on government policy towards 
the transportation sector. I study the interaction between oil 
price changes and the share of petroleum-based transportation 
in 10 OECD countries, in order to determine if this has a 
significant effect on the macroeconomic impacts of oil price 
shocks . 

The second essay sets out to test the effects of human 
capital on growth. I consider how initial levels of human 
capital, growth of human capital, government consumption, 
investment, political instability, and public infrastructure 
influence economic growth. I do so using a sample of 50 
countries during the guarter century from 1960 to 1985. I have 
assembled a rich data set that includes relevant variables 
that have not previously been put to use. 

The third essay uses patent data to study relative the 
contribution of technology creation and technology transfer to 
growth. Growth is fueled by both innovation and imitation. 
Imitation by newly industrialized countries (NICs) of 
technologies developed in the advanced countries turns out to 
be the main source of growth. A related point is that primary 
research in advanced countries can be imported by less 
developed countries (LDCs) , thereby permitting adaptive 
invention, rather than expensive original research. To examine 



6 
these ideas, this essay develops measures of imitation and 
adaptation. 

It is clear that accumulation of human capital by LDCs is 
essential to the successful acquisition of foreign 
technologies. Therefore, a close link exists between growth of 
human capital and successful technological imitation. This may 
well explain another part of the variation in growth among 
nations, but it is beyond the scope of this thesis. 

Chapter 5 summarizes the dissertation and explores the 
possibilities for further work in this rapidly advancing and 
important area of research. 



CHAPTER 2 

OIL PRICE CHANGES AND GNP FLUCTUATIONS 

Introduction 

The oil price shocks of the seventies seem to dominate 

the fortunes of the industrialized countries. Before 1973 most 

of these countries had experienced healthy economic growth and 

tolerable levels of inflation. By 1974, all these economies 

were in recession, with double-digit inflation plaguing all 

but West Germany and negative growth afflicting the United 

States, Japan, and the United Kingdom. Inflation slowed and 

growth resumed by 1978. However, the oil price shock of 1980 

appears to have brought renewed stagflation. 

Several writers have suggested that oil price shocks 
caused the recessions of the 1970s. Hamilton (1983) suggests 
that oil prices Granger-caused output and unemployment even in 
the period 1947 to 1972 as well as afterwards; evidence on the 
exogeneity of oil prices is provided in Hamilton (1983, 1985). 
A connection between disruptions in the energy sector and the 
economy is documented in Rasche and Tatom (1981) , Santini 
(1985) and Gisser and Goodwin (1986) . 

Other empirical work attempts to determine channels 
through which oil shocks affect macroeconomic activity. Mork 
(1988) provides a useful taxonomy of these efforts. Rasche and 



8 
Tatom view the shocks as aggregate supply shocks which affect 
potential GNP, while Pierce and Enzler (1974) emphasize the 
role of aggregate demand effects in propagating these shocks. 
Gordon (1975), Mork and Hall (1981) and Mork (1985) assert the 
importance of both aggregate supply and demand to the effects 
of oil shocks. 

Another view is motivated by the findings of Lilien 
(1982), Davis (1985) and Loungani (1986a, 1986b). Lilien found 
that the dispersion of employment growth across industries, a, 
is positively correlated with aggregate unemployment. Davis 
and Loungani subseguently demonstrated that oil price shocks 
are the predominant source of movements in a. That finding is 
consistent with a sectoral reallocation story oil price shocks 
alter relative prices across different sectors of the economy. 
The sectoral reallocation of production and employment entails 
temporary unemployment and recession. Recent work by Hamilton 
(1988) strengthens the theoretical underpinnings of this view. 
This chapter provides cross - country evidence on the 
relationship between oil price changes and real GNP. The same 
issue is examined in Darby (1982) and Burbidge and Harrison 
(1984), but with two differences. First, previous work has 
examined each nation in isolation. I use pooled data from 1960 
to 1987 for a Group of Ten OECD countries (Group of Seven plus 
Australia, Netherlands, and Spain). Second, I attempt to 
explain why the impact of oil prices on real GNP differs 
across countries. 



9 
The basic hypotheses that I wish to test are 
(i) that there is a negative correlation between oil price 
changes and real output, 

(ii) that the price elasticity for the transportation sector 
is smaller in absolute magnitude than for the other sectors, 
and therefore 

(iii) that the impact of oil price changes on macro activity 
depends on the share of transportation in oil consumption. 

This explanation is distinct from the sectoral 
reallocation. The second and third hypotheses contain another 
interpretation of oil price shocks. They have been suggested 
by Danilo Santini, who presents some evidence in favor of 
them. He points out there is evidence in favor of the view 
that the price elasticity of transportation is relatively low: 

The statistical results . . . tend to confirm the 
present descriptive analysis of the relative 
difficulty of substituting for oil in 
transportation. The estimates for both short and 
long-run elasticity for transportation are 
substantially smaller in absolute magnitude than 
for the other sectors, (p. 7) 

Santini suggests the importance of share of the 
transportation sector in total oil consumption in the 
industrialized countries: 

The fundamental arguments of this paper can be 

summarized as follows: 

-Because of its inability to economically and 

rapidly substitute nonpetroleum fuels 

transportation historically has been less able to 

reduce petroleum consumption than have other 

sectors of industrialized economies. 

-Because of transportation's fuel inflexibility 

industrialized nations that devoted a greater share 

of their total petroleum consumption to 



10 

transportation had greater difficulty reducing oil 
consumption after the 1978-1981 crude oil price 
run-up. (p. 10) 

The remainder of this chapter is organized as follows. 

The second Section summarizes trends in macroeconomic activity 

in our 10 countries, while the third Section presents data on 

differences in the consumption of oil by sector in these 

countries. The fourth Section specifies the regressions. 

Empirical results are presented in the fifth Section, while 

the last Section is a summary and conclusion. 

Trends in Macro Activities 

The growth rate has fallen over the last 20 years in the 

advanced countries. Productivity growth in these countries 

averaged 4.0 percent per year during 1960-68, 3.1 percent 

during 1968-73, 1.5 percent during 1973-79, and 1.6 percent 

during 1979-87. In each of the Group of Seven countries, 

productivity growth during the 1970s and 1980s was about 50 

percent less than that attained during 1960s. Growth in real 

GDP also fell during the same years. Real GDP growth averaged 

5.0 percent per year during 1960-68, 4.4 percent during 1968- 

73, 2.7 percent during 1973-79, and 2.6 percent during 1979- 

87. Corresponding to the reduction in growth, unemployment 

climbed from an average of 2.8 percent during 1964-67 to 3 . 3 

percent during 1968-73 and finally to 5.0 percent during 1974- 

79. But there are fairly wide differences in real GDP growth 

in the set of countries. Real GDP growth was 3.6 percent per 

year in Japan during the years 1973 to 1979, 2.4 percent in 



11 

the United States, 2.3 percent in Germany, and only 1.5 
percent in the United Kingdom (Table 2-1) . 

The diversity in macro performance among countries calls 
for an explanation. I examine the role of transportation in 
accounting for the differences. 

Consumption of Oil by Different Sectors: OECD Evidence 
The impact of oil price changes on macro activity 
differed across countries. For instance, Japan, though 
dependent on foreign oil to a much greater extent than the 
U.S., did not experience a recession as severe as the U.S. 
(Table 2-1) . 

Table 2-2 pursues the transportation cost hypothesis. It 
shows that the share of the transportation sector in total oil 
consumption differs in a way that is consistent with the 
American and Japanese experience. For instance, between 1960 
and 1987, Japan had a mean transport share of 29 percent, the 
United States 58 percent, Canada 48 percent, France 34 
percent, West Germany 31 percent, and the United Kingdom 44 
percent. At the same time there was wide dispersion in the 
average share of the industrial sector in total oil 
consumption among these countries. For example, Japan's 
average share was 4 8 percent, the U.S. 2 5 percent, Germany 3 
percent, and the U.K. 39 percent. 

Except for transportation, most sectors seem to be able 
to substitute non-oil-based fuels for petroleum products. The 
inflexibility of transportation makes its costs more 



12 
vulnerable to oil price shocks. West Germany and Japan, which 
are the nations with the lowest share of oil demand accounted 
for by the transportation sector, both managed to reduce total 
oil consumption by a greater percentage, and did not 
experience a recession in 1980 or 1981. This suggests that the 
size of a nation's transportation sector may interact with the 
oil price changes to raise costs and retard productivity. 

Model and Methodology 
Since there is no accepted structural model in this area, 
I will formulate an empirical model which is consistent with 
earlier work while permits a test of the hypotheses. 

First I investigate the effect of oil price shocks on GNP 
growth to see whether my result is consistent with pervious 
empirical work. I regress real GNP growth on a constant, the 
rate of change in the real price of oil, the rate of change in 
the money base, and the rate of change in government 
consumption. I also include lags of all the independent 
variables. 

Next I turn to estimation of the price elasticity of 
demand in the transport and industry sectors. I accomplish 
this by regressing the growth rate of transportation and 
industry consumption on oil price changes and other 
macroeconomic variables. 

Finally I analyze the correlation between GNP growth and 
the interaction of oil price changes and mean share of 
transportation's consumption of oil. I examine this relation 






13 



Table 2-1 

Key Macroeconomic Indicators 

(Year to year percentage changes) 





Average 


Average 


Average 


Average 


Average 




1960-68 


1968-73 


1973-79 


1979-87 


1960-87 


REAL GDP: 












United States 


4.5 


3.2 


2.4 


2.6 


3.2 


Japan 


10.2 


8.7 


3.6 


3.8 


6.5 


Germany 


4.1 


4.9 


2.3 


1.4 


3.0 


France 


5.4 


5.5 


2.8 


1.7 


3.7 


United Kingdom 


3.1 


3.3 


1.5 


1.8 


2.4 


Italy 


5.7 


4.5 


3.7 


2.2 


4.0 


Canada 


5.5 


5.4 


4.2 


2.9 


4.4 


Total of above 












countries 


5.0 


4.4 


2.7 


2.6 


3.7 


Total OECD 


5.0 


4.5 


2.7 


2.5 


3.7 


REAL GDP PER PERSON 










EMPLOYED: 












United States 


2.6 


1.0 


0.0 


1.0 


1.2 


Japan 


8.5 


7.6 


2.9 


2.9 


5.4 


Germany 


4.2 


4.1 


2.9 


1.5 


3.1 


France 


4.9 


4.3 


2.5 


1.9 


3.4 


United Kingdom 


2.7 


3.1 


1.3 


1.8 


2.2 


Italy 


6.3 


4.9 


2.8 


1.8 


3.9 


Canada 


2.6 


2.5 


1.3 


1.1 


1.8 


Total of above 












countries 


4.0 


3.1 


1.5 


1.6 


2.6 


Total OECD 


4.0 


3.3 


1.6 


1.6 


2.6 




Average 


Average 


Average 


Average 


Average 




1964-67 


1968-73 


1974-79 


1980-87 


1964-87 


STANDARDIZED 












UNEMPLOYMENT RATE 












United States 


4.2 


4.6 


6.7 


7.6 


6.1 


Japan 


1.2 


1.2 


1.9 


2.5 


1.8 


Germany 


0.6 


1.0 


3.2 


6.0 


3. 1 


France 


1.7 


2.6 


4.5 


8.9 


5.0 


United Kingdom 


2.5 


3.3 


5.0 


10.5 


6. 


Italy 


5.1 


5.7 


6.6 


9.5 


7.1 


Canada 


3.9 


5.4 


7.2 


9.7 


7.0 


Total of above 












countries 


2.8 


3.3 


5.0 


7.0 


4.9 


Total OECD 


2.7 


3.2 


4.9 


7.5 


5.0 



Source: OECD, Historical Statistics 1960-1987, Paris, 1989. 



14 









Table 


2-2 










Summary of Oil Consumption 
(Percentage Share of Total 


by Sectors 
Consumption) 






Country/Sector 


1973 


1974 


1975 


1976 


1977 


1978 


1979 


1980 


U.S.A. 














Transportation 

Industry 

Others 


58 
23 

19 


59 
23 

18 


61 
22 

17 


59 
23 
18 


58 
25 

17 


59 
25 
16 


59 

27 
14 


61 
25 
14 


JAPAN 


















Transportation 

Industry 

Others 


23 
55 
22 


24 
54 
22 


27 
49 

24 


27 
50 
23 


28 

48 

24 


29 

47 
24 


29 

47 
24 


33 
43 

21 


GERMANY 


















Transportation 

Industry 

Others 


25 
33 
42 


27 
33 
40 


29 
30 
41 


28 

29 

43 


30 
28 
42 


30 
27 
43 


30 
28 
42 


34 
27 
39 


FRANCE 


















Transportation 

Industry 

Others 


27 
34 
39 


28 
36 
36 


31 
34 
35 


32 
33 
35 


33 
33 

34 


33 
31 
36 


33 
34 
33 


36 
33 

31 


CANADA 


















Transportation 

Industry 

Others 


44 
27 
29 


45 
27 
28 


48 
25 
27 


49 
23 
28 


49 
25 
26 


50 
26 
24 


51 
27 
22 


53 
25 
22 


UNITED KINGDOM 


















Transportation 

Industry 

Others 


38 
45 
17 


39 
44 
17 


41 
41 
18 


42 
40 
18 


43 
39 
18 


45 
37 
18 


45 
37 
18 


52 
31 
17 


ITALY 


















Transportation 

Industry 

Others 


27 
41 
32 


26 
42 
32 


29 

36 
35 


29 
38 
33 


32 
38 
30 


34 
34 
32 


35 
35 
30 


37 
33 
30 



15 



Table 2-2 — continued 



Country/Sector 


1981 


1982 


1983 


1984 


1985 


1986 


1987 




U.S.A. 


















Transportation 

Industry 

Others 


63 
24 
13 


64 
23 
13 


66 
21 
13 


66 
21 

13 


67 
21 
12 


66 
22 

12 


66 
21 

13 




JAPAN 


















Transportation 

Industry 

Others 


34 
40 
26 


36 
38 
26 


36 
38 
26 


35 
38 
27 


37 
37 
26 


37 
37 
26 


37 
37 
26 




GERMANY 


















Transportation 

Industry 

Others 


37 
25 
38 


39 

24 
37 


39 
26 
35 


40 
24 
36 


39 
22 
39 


39 
21 
40 


42 
21 

37 




FRANCE 


















Transportation 

Industry 

Others 


40 
27 
33 


42 
27 
31 


42 
27 
31 


44 
25 
31 


45 
25 
30 


46 
23 

31 


46 
24 
30 




CANADA 


















Transportation 

Industry 

Others 


55 
26 
19 


54 
23 
23 


55 
22 
23 


56 
23 
21 


57 
24 
19 


57 
25 
18 


57 
25 
18 




UNITED KINGDOM 


















Transportation 

Industry 

Others 


52 
30 
18 


54 
29 

17 


56 
28 
16 


58 
26 
16 


59 
25 
16 


60 
26 

14 


62 
24 

14 




ITALY 


















Transportation 

Industry 

Others 


38 
32 
30 


41 
29 
30 


41 
30 
29 


42 

27 
31 


44 
27 
29 


46 
26 
28 


47 
26 
27 




Source: Energy Bal 


ances of 


OECD 


Countries, 


IEA. Pa 


ris, 


1988. 








16 
by estimating using a regression model which includes 
macroeconomic controls, in order to test whether the effect is 
a direct result of the interaction between oil price changes 
and the share of transport oil consumption. 
I estimated the following regressions: 

DGNP = a x + b, DOP + Cl DOPi + d x DRM + e t DRM t + f l DGC + 
9i nGC t + ki Di + Ul i = 1,2,... 

DGNP = a 2 + b 2 MINTER + h t MINTERi + d 2 DRM + Oi DRMi + 
f 2 DGC + q, DGCi + 1, Dl + u 2 i = 1,2,... 

DGNP = a 5 + b 5 DTINTER + x t DTINTERi + d 5 DRM + y, DRM t + 
f 5 DGC + 2i DGC L + Pl D t + u 5 i = 1,2,... 

where DGNP is GNP or GDP growth, DOP is relative oil-price 
changes, DOPi is lagged DOP, DRM is growth of money base, DRMi 
is lagged DRM, DGC is change in government consumption, DGCi 
is lagged DGC, D 4 are country dummy variables, MINTER is the 
interaction of mean share of transport oil consumption and 
relative oil price changes (DOP x MEAN) , MINTERi is lagged 
MINTER, DTINTER is the interaction of real oil price (ROP) and 
share of transport's oil consumption (SHARE), namely (ROP x 
SHARE changes) , DTINTER t is lagged DTINTER, and Ui are the 
residual components. We hypothesize that bl, b2 , b5, ci, h , 
and Xi should be negative. Also, we expect that the absolute 
value of b3 < b4 and r t < v Lr respectively. All the variables 
except ROP, MEAN, and SHARE are in logarithmic form. 



17 
For the second hypothesis I estimated 

DTTR = a 3 + b 3 DOP + r 4 DOPi + d 3 DRM + s t DRMi + f 3 DGC + 

tj DGCi + m d D d + u 3 i = l , 2 , . . . 

DTIN = a A + b, DOP + v A DOPi + d 4 DRM + j L DRMi + f 4 DGC + 
Wi DG^ + n t Di + u, i = 1,2,... 

where DTTR is growth share of transport's oil consumption, and 
DTIN is growth share of industry's oil consumption. 

Empirical Results 
I estimate these eguations using pooled data for the 
Group of Ten countries over the period 1960 to 1987. Details 
of these data are given in Appendix A. In general, the data 
provide strong support for the idea that an increase in the 
price of oil decreases GNP growth. 

First I estimate the price elasticity of demand. Tables 
2-3 and 2-4 present the results for the price elasticity of 
industry and transportation sectors, respectively, when the 
other macroeconomic variables are included in the eguation 
(for the Group of Seven) . As we expected the price elasticity 
of the industrial sector is higher than the transportation 
sector and both estimated coefficients are statistically 
significant at better than a 99 percent level. 

Table 2-5 and 2-6 present the results for transport and 
industry's oil consumption growth within the Group of Ten. As 
with Tables 2-3 and 2-4, column 1 of Tables 2-5 and 2-6 show 
the relationship between the transportation sector oil 
consumption and DOP and also between industry sector oil 



18 
consumption and DOP, respectively. Again as we expected, the 
price elasticity of industry is greater than the elasticity 
for the transport sector. Column 2 of Tables 2-5 and 2-6 
substitute GNP growth for the other macroeconomic variables. 
In this case, the oil price elasticity for industrial sector 
is markedly higher than transport sector. Also, both estimated 
coefficient are statistically significant at better than a 
ninety-nine percent level. 

Thus, the results of Tables 2-3, 2-4, 2-5 and 2-6 are in 
accord with the idea that the transport sector is not 
flexible, while the industrial sector is much more flexible. 
In other words, whenever there is an increase in the price of 
oil, the industrial sector can substitute other sources of 
energy, but it is almost impossible for the transport sector 
to do it. 

Table 2-7 contains the basic set of estimated 
relationships for the Group of Seven. Column 1 illustrates the 
effect of oil price changes on growth. The coefficient on DOP 
(relative oil price changes) is statistically significant at 
the 94 percent level and the estimated coefficient on D0P1 
(lagged DOP) is statistically significant at better than a 99 
percent level. An F test rejects the null hypothesis that the 
impact of DOP and D0P1 is zero with F(2, 151) = 8.91. 

Column 2 changes the DOP variable to MINTER (interaction 
of transport mean share and oil price changes) to allow for 
the influence of differences in transport share across 



19 
countries. Compared to column 1 the estimated coefficient on 
the first oil variable increased only slightly from 0.011 to 
0.028. 

Column 3 shows the combined effect of DOP and MINTER. But 
the estimated coefficients on DOP and MINTER are not 
statistically significant, because of multicollinearity. 

Columns 4, 5, 6 and 7 of table 2-7 present analogous 
results for the effect of lagged oil price shocks. We see that 
there are no significant changes. 

Thus, the results of Table 2-7 are congruent with 
negative shock interpretation of oil price changes. However, 
the transportation cost hypothesis is rejected. 

To check for sensitivity of these conclusions to the set 
of countries included, Table 2-8 contains a similar set of 
estimated relationships between GNP growth and oil price 
changes in the Group of Ten. This time, however I have added 
another variable DTINTER, which measures the interaction of 
real oil price and share of transport's oil consumption. 

Column 1 illustrates the effect of DTINTER with three 
lags and DOP with three lags again on the GNP growth. The 
estimated coefficient of DOP is not statistically significant, 
but the estimated coefficient on DOPl (lagged DOP) is 
statistically significant at better than a 99 percent level. 
None of coefficients on DTINTER are statistically significant. 
Column 2 shows the relationships between DOP and GNP 
growth rate. Again the estimated coefficient on DOP is not 



20 
statistically significant, but DOP1 is significant at better 
than 99 percent level. Also, an F test rejects the null 
hypothesis that the impact of DOP and DOP1 is zero with 
F(2,194) = 7.94. 

Column 3 exhibits the effect on GNP growth of only 
contemporaneous and lagged DTINTER. Again none of the 
estimated coefficients are statistically significant. 

Column 7 to 11 of Table 2-8 contain the set of estimated 
relationships which are the same as those in Table 2-7 but 
this time for the Group of Ten. In column 7 as with column 3 
of Table 2-7 none of the estimated coefficients of DOP and 
MINTER are significant. In column 8 , contrary to column 2 of 
Table 2-7, the coefficient of MINTER is not statistically 
significant. However, even though the estimated coefficient on 
MINTER1 is reduced -from 0.055 to 0.048- it remains 
significant at better than a 99 percent level. 

The results of Table 2-8 agree with the shock 
interpretation of oil price changes, but reject the 
transportation cost interpretation. 

Conclusion 
There exists a strong, negative correlation between oil 
price changes and GNP growth. Darby (1982) has established 
this correlation for eight major OECD countries data over the 
period 1973-1975 and Mork et al.(l989) has attained similar 
cross-section results for a sample of six major OECD 
countries. 



21 
The contribution of this paper is to expand this list of 
results and to offer evidence against the "direct causation" 
hypothesis that a high share of transportation of total oil 
consumption drags down GNP growth. Tables 2-7 and 2-8 contain 
results which establish a negative correlation between the oil 
price changes and GNP fluctuations for 10 major OECD 
countries. However, the results in these tables do not support 
the idea that a higher share of transportation sector lowers 
the GNP growth rate in the face of oil price changes. 

Tables 2-3, 2-4, 2-5, and 2-6 contain point estimate 
results which are consistent with the view that the price 
elasticity for transportation sector is lower in absolute 
magnitude than other sectors. Further, supporting Santini's 
descriptive arguments, the percentage of reduction in oil use 
estimated to have occurred through fuel substitution is far 
lower for transportation than for the industrial sector. 

Finally, Table 2-9 which presents the effect on the 
difference between growth of transport's and industry's oil 
consumption of DOP and D0P1. This again supports the 
hypothesis of a lower price elasticity of demand in the 
transport sector. 



22 

Table 2-3 

Industry Consumption of Oil Growth in Group of Seven 

(dependent variable - DTIN) 



Variables 


Coefficients 


St. Error 


Prob> [ T | 


INTERCEP 


-0.011 


0.029 


0.7082 


DOP 


-0.068 


0.024 


0.0066 


D0P1 


-0.123 


0.025 


0.0001 


DRM 


0.209 


0.120 


0.0834 


DRM1 


0.165 


0.123 


0.1825 


DGC 


0.204 


0.211 


0.3342 


DGC1 


-0.316 


0.195 


0.1078 


R 2 


0.2524 















23 

Table 2-4 

Growth of Transport Oil Consumption in Group of Seven 

(dependent variable - DTTR) 



Variables 


Coefficients 


St. Error 


Prob> j T | 


INTERCEP 


0.020 


0.011 


0.0710 


DOP 


-0.042 


0.009 


0.0001 


D0P1 


-0.025 


0.010 


0.0132 


DRM 


0.137 


0.048 


0.0051 


DRM1 


0.086 


0.050 


0.0880 


DGC 


0.091 


0.086 


0.2917 


DGC1 


-0.054 


0.080 


0.5008 


R 2 


0.2896 










24 

Table 2-5 

Transport's Oil Consumption Growth in the Group of Ten 

(dependent variable - DTTR) 



VARIABLE 



INTERCEP 

DOP 

D0P1 

D0P2 

D0P3 

DRM 

DRM1 

DRM2 

DRM3 

DGC 

DGC1 

DGC2 

DGC 3 

DGNP 

DGNP1 

DGNP2 

DGNP3 



(1) 






.033 


(0 


.017) 


-0 


.030 


(0 


.009) 


-0 


.024 


(0 


.009) 


-0 


.010 


(0 


.012) 


-0 


.025 


(0 


.012) 





.068 


(0 


042) 





046 


(0. 


043) 


-0. 


026 


(0. 


044) 


-0. 


082 


(0. 


044) 


0. 


101 


(0. 


077) 


0. 


055 


(0. 


082) 


0. 


005 


(0. 


083) 


-0. 


034 


(0. 


071) 



0.21 



(2) 



-0.007 

(0.010) 

-0.025 

(0.007) 

-0.006 

(0.007) 

0.010 
(0.011) 

0.006 
(0.011) 



0.668 
(0.113) 

0.302 
(0.118) 

0.284 
(0.118) 

0.178 
(0.103) 

0.42 



(3) 



0, 
(0, 
-0, 
(0, 
-0. 



026 

007) 

028 

009) 

027 



(0.009) 



-0.026 
(0.011) 

0.086 
(0.037) 



-0.014 
(0.040) 



0.095 
(0.062) 



0.17 



NOTE: Figures in parentheses represent the standard 



error. 



25 
Table 2-6 



Oil Price Changes and Industry's Oil Consumption Growth 
in the Group of Ten (dependent variable - DTIN) 



VARIABLE 


(1) 


(2) 


(3) 




INTERCEP 


0.130 


-0.078 


0.017 






(0.048) 


(0.026) 


(0.019) 




DOP 


-0.034 


-0.054 


-0.043 






(0.026) 


(0.019) 


(0.025) 




D0P1 


-0.090 


-0.051 


-0.100 






(0.026) 


(0.020) 


(0.025) 




DOP2 


-0.032 
(0.034) 


0.016 
(0.029) 






DOP3 


-0.021 


0.047 


-0.028 






(0.034) 


(0.028) 


(0.031) 




DRM 


0.017 
(0.116) 




0.150 
(0.105) 




DRM1 


0.086 
(0.119) 








DRM2 


-0.043 
(0.121) 




0.091 
(0.112) 




DRM3 


-0.100 
(0.123) 








DGC 


-0.277 
(0.213) 




-0.114 
(0.174) 




DGC1 


0.209 
(0.226) 








DGC2 


0.082 
(0.231) 








DGC3 


-0.173 
(0.197) 








DGNP 




1.812 
(0.295) 






DGNP1 




0.987 
(0.309) 






DGNP2 




0.224 
(0.309) 






DGNP3 




0.920 
(0.269) 






R 2 


0.19 


0.46 


0.12 





NOTE: Figures in parentheses represent the standard error. 



26 

Table 2-7 

GNP Growth and Oil Price Changes in the Group of Seven 
(dependent variable - DGNP) 



VARIABLE (1) (2) (3) (4) (5) (6) (7) 

INTERCEP 0.021 0.022 0.022 0.025 0.025 0.029 0.024 

(0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) 

DOP -0.011 0.001 0.003 

(0.005) (0.023) (0.023) 

D0P1 -0.022 -0.008 -0.008 

(0.006) (0.023) (0.023) 

DRM 0.082 0.085 0.084 0.096 0.097 0.095 0.087 

(0.029) (0.028) (0.029) (0.028) (0.028) (0.029) (0.029) 

DRM1 0.045 0.045 0.045 0.053 039 

(0.030) (0.030) (0.030) (0.031) (0.'o30) 

DGC 0.042 0.045 0.045 0.0006 0.015 

(0.052) (0.052) (0.052) (0.052) (0.050) 

DGC1 -0.083 -0.093 -0.090 -0.058 -0.060 -0.131 -0.088 

(0.048) (0.047) (0.049) (0.042) (0.040) (0.048) (0.048) 

MINTER -0.028 -0.032 -0.028 -0.019 -0.022 

(0.014) (0.057) (0.057) (0.013) (0.014) 

MINTER1 -0.055 -0.035 -0.032 -0.052 -0.053 

(0.015) (0.057) (0.058) (0.014) (0.015) 

^ °- 33 0-33 0.33 0.31 0.31 0.27 0.31 

NOTE: Figures in parentheses represent the standard error. 






27 
Table 2-8 



GNP Growth and Oil Price Changes in the Group of Ten 
(dependent variable - DGNP) 



VARIABLE 


(1) 


(2) 


(3) 


(4) 


(5) 


(6) 


INTERCEP 


0.036 


0.037 


0.051 


0.030 


0.031 


0.040 




(0.010) 


(0.009) 


(0.009) 


(0.003) 


(0.004) 


(0.003) 


DOP 


-0.008 


-0.005 




-0.010 


-0.006 






(0.005) 


(0.005) 




(0.005) 


(0.005) 




D0P1 


-0.021 


-0.021 




-0.024 


-0.025 






(0.005) 


(0.005) 




(0.005) 


(0.005) 




DOP2 


-0.003 


-0.005 




-0.009 








(0.007) 


(0.007) 




(0.006) 






D0P3 


-0.013 
(0.007) 


-0.014 
(0.007) 






-0.015 
(0.006) 




DTINTER 


0.0001 




0.0001 


0.0001 




0.00007 




(0.0001) 




(0.0001) 


(0.0001) 




(0.0001) 


DTINTER1 


-0.00004 




-0.0001 


-0.00006 




-0.0002 




(0.0001) 




(0.0001) 


(0.0001) 




(0.0001) 
-0.0003 


DTINTER2 


-0.0002 




-0.0001 


-0.0003 






(0.0003) 




(0.0003) 


(0.0002) 




(0.0002) 


DTINTER3 


-0.0001 




-0.0002 








(0.0003) 




(0.0003) 








DRM 


0.057 


0.058 


0.071 


0.060 


0.056 


0.083 




(0.023) 


(0.023) 


(0.024) 


(0.021) 


(0.021) 


(0.021) 


DRM1 


0.028 


0.023 


0.032 








(0.024) 


(0.024) 


(0.025) 








DRM2 


-0.040 


-0.042 


-0.046 


-0.042 


-0.041 


-0.034 




(0.025) 


(0.024) 


(0.025) 


(0.022) 


(0.022) 


(0.022) 


DRM 3 


0.016 
(0.025) 


-0.017 
(0.025) 


0.017 
(0.025) 






1 / 


DGC 


0.034 


0.038 


-0.009 


0.039 


0.031 






(0.043) 


(0.043) 


(0.043) 


(0.035) 


(0.035) 




DGC1 


-0.067 


-0.064 


-0.096 






-0.084 




(0.046) 


(0.046) 


(0.046) 






(0.033) 


DGC2 


-0.033 


-0.037 


-0.058 








(0.047) 


(0.046) 


(0.047) 








DGC3 


0.036 
(0.040) 


0.033 
(0.040) 


0.023 
(0.041) 








R 2 


0.34 


0.33 


0.27 


0.27 


0.27 


0.21 









28 



Table 2-8 — continued 





(7) 


(8) 


(9) 


(10) 
0.035 


(11) 
0.031 


(12) 


INTERCEP 


0.040 


0.039 


0.030 


0.036 




(0.009) 


(0.010) 


(0.003) 


(0.005) 


(0.004) 


(0.008) 


DOP 


-0.014 




-0.021 


0.0002 




-0.009 




(0.021) 




(0.021) 


(0.020) 




(0.005) 


DOP1 


0.0004 




-0.003 


-0.014 




-0.023 




(0.022) 




(0.022) 


(0.020) 




(0.005) 


DOP2 


-0.048 
(0.029) 




-0.047 
(0.027) 








DOP3 


-0.056 
(0.028) 












MINTER 


0.020 


-0.012 


0.028 


-0.013 


-0.012 






(0.048) 


(0.011) 


(0.048) 


(0.047) 


(0.011) 




MINTER1 


-0.050 


-0.048 


-0.050 


-0.017 


-0.056 






(0.050) 


(0.012) 


(0.049) 


(0.047) 


(0.011) 




MINTER2 


0.102 
(0.066) 


-0.003 
(0.016) 


0.082 
(0.062) 








MINTER3 


0.097 
(0.065) 


-0.024 
(0.016) 






-0.029 
(0.014) 




DRM 


0.061 


0.062 


0.062 


0.053 


0.058 


0.061 


DRM1 


(0.024) 

0.009 
(0.024) 


(0.023) 

0.021 

(0.024) 


(0.021) 


(0.023) 


(0.021) 


(0.022) 

0.018 

(0.023) 


DRM2 


-0.047 


-0.039 


-0.041 




-0.041 




(0.025) 


(0.024) 


(0.022) 




(0.023) 




DRM3 


0.011 
(0.025) 


0.017 
(0.025) 






» / 




DGC 


0.058 


0.041 


0.040 




0.029 


0.043 




(0.044) 


(0.043) 


(0.035) 




(0.035) 


(0.040) 


DGC1 


-0.074 


-0.070 




-0.048 




\ * w / 

-0.097 


DGC2 


(0.046) 

-0.048 

(0.047) 


(0.046) 

-0.047 

(0.047) 




(0.034) 




(0.038) 


DGC 3 


0.045 
(0.040) 


0.026 
(0.040) 










R 2 


0.35 


0.32 


0.27 


0.24 


0.26 


0.30 



NOTE: 



Figures in parentheses represent the standard 



error. 









29 
Table 2-9 



Difference Between Growth of Transport's and Industry's Oil 

Consumption in Group of Seven 

(dependent variable - DTIS) 



Variables 


Coefficient 


St. Error 


Prob> | T j 


INTERCEP 


0.019 


0.025 


0.4639 


DOP 


0.024 


0.019 


0.2163 


D0P1 


0.097 


0.020 


0.0001 


DRM 


0.010 


0.102 


0.9177 


DRM1 


-0.086 


0.106 


0.4165 


DGC 


-0.241 


0.189 


0.2055 


DGC1 


0.371 


0.178 


0.0394 


R 2 


0.2216 










CHAPTER 3 



HUMAN CAPITAL AND GOVERNMENT POLICIES 
Introduction 
Recent theoretical developments, which emphasize the 
endogeneity of growth and thus its sensitivity to policy, have 
caused economists to recognize that government policies toward 
investment in human and physical capital are significant for 
growth (Uzawa, 1965; Arrow, 1962; Solow, 1956; Lucas, 1988; 
King and Rebelo, 1990; Romer, 1986, 1990). There now exists 
some empirical research which has studied the managing of 
economic growth. A good example is Barro (1991) , who, using 
cross-country data, examines several of the factors addressed 
in the endogenous growth literature. In this paper, in line 
with Barro, I use the insights obtained from recent 
theoretical work to investigate empirical relationships 
between per capita output growth and investments in human and 
physical capital. But, in contrast to previous work, I use new 
human capital constructs in order to probe the data more 
deeply. 

There have been two major types of growth models. Older, 
exogenous growth models are characterized by the assumption of 
diminishing returns, and so any permanent growth in per capita 
income stems from exogenous technological change (Solow, 

30 



31 
1956) . Endogenous growth models generate permanent growth in 
per capita income within the system by assuming constant or 
increasing returns to reproducible factors of production. 
These models allow us to study the effects of government 
policies on investment incentives and hence long-run growth. 
A brief description of this approach is useful. 

Paul Romer (1986, 1990) stresses the role played by the 
accumulation of disembodied knowledge, as opposed to human 
capital. In his 1986 paper, he proposes a new model in which 
growth takes place because the production function is subject 
to increasing returns to scale through knowledge spillovers. 
The growth of consumption is driven permanently above zero 
because knowledge pushes the marginal product of capital 
permanently above the rate of time preference. 

In his later paper, Romer explicitly allows for a 
permanent effect on the rate of growth stemming from the stock 
of initial human capital. His model suggests a fanning out of 
incomes among countries: very low levels of human capital 
result in very slow growth in underdeveloped economies; very 
high levels of human capital cause very rapid growth in 
developed economies. The same reasoning suggests that pooling 
of human capital would raise world growth rates. 

Lucas (1988) takes a different approach to the role of 
human capital in growth models. He postulates both an internal 
and external effect of human capital. The internal effect is 
the impact of an individual's human capital on his private 



32 
marginal product. The external effect, which is identified 
with the country's average level of human capital, contributes 
to the productivity of all persons and factors of production. 
This model has less radical predictions for fanning out: 
economies that are initially poor will remain relatively poor, 
though their long-run rate of income growth will be the same 
as that of initially (and permanently) wealthier economies. A 
world consisting of such economies, each operating 
autarkically, would exhibit uniform rates of growth across 
countries and would maintain a perfectly stable ranking of 
income and wealth over time. 

This chapter is closely tied to the developments 
summarized above, especially those in Romer (1990). For the 
first time as far as I know, the role of human capital in 
cross-country growth is extensively studied using new and 
superior measures that enable us to separate stock and growth 
rate effects of human capital. In particular, I have been able 
to separate the initial level of mean years of schooling of 
the total population, from growth in mean schooling over a 
period of three decades. 1 This allows me to test several 
effects of human capital hitherto relegated only to theory. 

It is well-known that there are transitional dynamics as 
well as steady states associated with economic growth. The 
presence of a transition period is why most economists have 
employed long-run cross-sections. To "average out" the 
transitions and thus concentrate on the steady states, I use 






33 
pooled time-series cross-section data supplemented by control 
variables. I control for transitional effects in these data by 
using cyclical variables such as percent deviation of GDP from 
rest of the world GDP, investment, government consumption, and 
population growth, all of which are year specific. The data 
are a sample of 50 countries during the guarter century from 
1960 to 1985. This is a rich data set that combines relevant 
variables many of which have not been put to use for the 
purpose of studying growth. 

This chapter has three main concerns. The first is the 
connection between the initial stock of human capital, growth 
in human capital, and economic growth. As mentioned, I find 
that both measures of human capital contribute to a nation's 
growth . 

My second concern is with convergence of per capita 
income. We have seen that there are different and often 
contradictory ideas about convergence of income among nations. 
My empirical results, in line with Barro (1991), confirm 
convergence. 

My third concern is with government policies towards 
consumption and investment. Drawing on neoclassical growth 
models, Barro (1990) predicts a negative relationship between 
the size of transfer payments, which he calls government 
consumption, and economic growth. He distinguishes between 
productive and nonproductive government expenditures and their 
repercussions on growth. His findings show a negative 



34 
correlation between government consumption and growth. I find 
the same negative effect of government consumption on economic 
growth . 

The second section discusses the important new data on 
human capital and describes all the variables. Empirical 
results are reported in the third section. The fourth section 
draws implications of the findings for cross-country growth. 
The final section is a summary and conclusion. 

Data and Expected Results 

The GDP data and its components are from Summers and 
Heston (1988). These data include real GDP, government 
consumption, investment, and population for 13 countries 
outside the Eastern Bloc. The data are annual and cover the 
period 1960 to 1985. The other data come from the United 
Nations, the World Bank, Banks (1990), Barro (1991), and other 
sources. Sources of variables used in this paper are 
documented in Table 3-1. 

I employ two sets of proxies for human capital which I 
believe to be improvements on any that have previously 
appeared. The first is collected from various UNESCO 
statistical yearbooks and serves as the basis for calculating 
mean years of schooling of the total population aged 25 years 
and older. 2 My second variable comes from Horn and Arriagada 
(1986) and consists of years of schooling for young workers 
alone. 









35 



Table 3-1 
Definitions and Sources of Variables 



Variable Name 



Definition and Source 



AFRICA 

ASSASSINATIONS 

GDP60 

GDPDEV 



GOV. CONSUMP. 



HUMAN CAPITAL GROWTH 
(UNWEIGHTED) 

HUMAN CAPITAL GROWTH 
(WEIGHTED) 



Dummy variable for sub-Saharan Africa 

Number of assassinations per year 
Source: Banks (1990) 
Real GDP per capita, 1960 
Source: Summers and Heston (1988) 

Magnitude of the deviation of GDP of each 
country from the sample mean of rest of 
the world over time 
Source: Summers and Heston (1988) 

Ratio of real government "consumption" 
expenditure to real GDP (exclusive of real 
military expenditure) 

Source: Summers and Heston (1988) USACDA 
(various years) 

Growth of average years of schooling over 

period of 20 years 

Source: UNESCO (various years) 



Human capital growth weighted by real 
expenditure per pupil with 10 years lagged 
Source: UNESCO (various years) Summers and 
Heston (1988) 
INITIAL HUMAN CAPITAL Mean years of schooling of total 



(UNWEIGHTED) 

INITIAL HUMAN CAPITAL 
(WEIGHTED) 



population 25 years old and over, 1960 
Source: UNESCO (various years) 



Initial Human Capital weighted by real 
educational expenditure per pupil 
Source: UNESCO (various years) Summers and 
Heston (1988) 
INTERNATIONAL AND 

CIVIL WAR CASUALTIES International plus civil war battle deaths 

per 10000 population 
Source: Small and Singer (1982) 
INTERNATIONAL AND CIVIL 



WAR YEAR (DUMMY) 



INVESTMENT 



Dummy for duration of international or 

civil wars 

Source: Small and Singer (1982) 

Ratio of real domestic investment (private 

plus public) to real GDP 

Source: Summers and Heston (1988) 



36 



Table 3-1 — continued 



Variable Name 



Definition and Source 



LAT. AMERICA 

% LITERATE 

MILITARY EXPEND. 
DEVIATION 

MIXED 

PER CAPITA GDP GROWTH 

POPULATION 

REAL MILITARY EXPEND. 



REAL OIL PRICE 
DEVIATION 



REVCOUPS 



SOCIAL 



TEACHER STUDENT RATIO 
(PRIMARY) 



TEACHER STUDENT RATIO 
(SECONDARY) 



URBANIZATION 



% WORK FORCE IN 
INDUSTRY 



Dummy variable for Latin America 

Percent literate 
Source: Banks (1990) 

Deviation from predicted value of real 
military expenditure over time 
Source: USACDA (various years) 

Dummy variable for mixed free 
enterprise/socialistic economic system 
Source: Barro (1991) 

Growth rate of real per capita GDP 
Source: Summers and Heston (1988) 

Population in thousands 

Source: Summers and Heston (1988) 

Ratio of real military expenditure to real 

GNP 

Source: USACDA (various years) 

Fluctuations of Saudi Arabia's real oil 
price over time as a basic price for OPEC 

Number of revolutions and coups per year 
Source: Banks (1990) 

Dummy variable for socialist economic 

system 

Source: Barro (1991) 

Teacher student ratio in primary schools 

each decade 

Source: World Bank (1988) 

Teacher student ratio in secondary schools 

each decade 

Source: World Bank (1988) 

Population, cities of 20000 and over per 

capita 

Source: Banks (1990) 

Percent work force in industry sector 
Source: Banks (1990) 



37 
Keeping in mind the framework established by the models 
already discussed, I expect a positive effect of both the 
stock of human capital and its growth on growth in per capita 
income. I also expect a positive relation between growth and 
the investment ratio. However, I expect negative effects of 
initial GDP, government consumption, political instability, 
and population growth. 

Empirical Results 
Descriptive Statistics 

Descriptive statistics are shown in Tables 3-2 and 3-3. 
Table 3-2 groups the countries by 1960 per capita GDP. One can 
see from this Table that middle income countries grow faster 
than others. Also, one observes that it is the rich nations 
that have the largest initial human capital. But it is the low 
income countries that have the highest percentage growth of 
human capital. 3 Finally, rich countries invest more in 
physical capital than poor countries in this sample. 

Table 3-3 organizes the data according to growth in per 
capita GDP. According to Table 3-3, the more rapidly growing 
economies are the countries with lower GDP per capita in 1960. 
In other words, poorer countries grow more rapidly, suggesting 
convergence. We also see that rapidly growing nations invest 
more than slowly growing countries, and the growth of their 
human capital exceeds human capital growth elsewhere. This 
suggests that the higher is the growth of human capital the 
higher is the growth of income. Finally, initial years of 



38 



Table 3-2 

Descriptive Statistics (1960-85) 

Ordered by Levels of 1960 GDP per Capita 



Variable 



Mean 



Standard Deviation 



TOP INCOME GROUP: 1 

Average Annual per Capita 2.11 

GDP Growth (%) 
Average Annual Growth of 1.62 

Human Capital (%) 
Weighted Average Annual 5.36 

Growth of Human Capital (%) 
GDP Level (1960) 4814 

Mean Years of Schooling (1960) 6.72 
Average Share of Investment (%) 2 5.3 
Average Share of Government 13.8 

Consumption in GDP (%) 

MIDDLE INCOME GROUP: 2 



1.35 

1.08 

2.94 

1224 
2.24 
5.43 
3.74 



Average Annual per Capita 

GDP Growth (%) 
Average Annual Growth of 

Human Capital (%) 
Weighted Average Annual 

Growth of Human Capital (%) 
GDP Level (1960) 
Mean Years of Schooling (1960) 
Average Share of Investment (%) 
Average Share of Government 

Consumption in GDP (%) 

BOTTOM INCOME GROUP: 3 

Average Annual per Capita 

GDP Growth (%) 
Average Annual Growth of 

Human Capital (%) 
Weighted Average Annual 

Growth of Human Capital (%) 
GDP Level (1960) 
Mean Years of Schooling (1960) 
Average Share of Investment (%) 
Average Share of Government 

Consumption in GDP (%) 



2.73 

2.19 

6.10 

1753 
3.94 
21.6 
15.9 



2.52 

3.87 

6.94 

716 
1.82 
16.8 
17.4 



1.47 

0.72 

2.96 

579 
2.07 
7.07 
6.37 



1.81 

1.20 

6.29 

196 
1.04 
6.46 
5.95 



Note: The sample is divided in thirds: 
(1) . Income level greater than 2932. 
(2) . Income level between 1062 and 2838 
(3). Income level less than 1012. 



39 



Table 3-3 
Descriptive Statistics (1960-85) 
Ordered by Growth Rates in GDP per Capita 



Variable 



Mean 



Standard Deviation 



RAPID GROWTH ECONOMIES 



.1 



Average Annual per Capita 4 . 18 

GDP Growth (%) 

Average Annual Growth of 2.95 

Human Capital (%) 

Weighted Average Annual 8.54 

Growth of Human Capital (%) 

GDP Level (1960) 1903 

Mean Years of Schooling (1960) 3.75 

Average Share of Investment (%) 2 4.6 

Average Share of Government 15.5 

Consumption in GDP (%) 

MODERATE GROWTH ECONOMIES: 2 



0.82 

1.58 

4.56 

1389 
2.24 
6.44 
4.46 



Average Annual per Capita 

GDP Growth (%) 
Average Annual Growth of 

Human Capital (%) 
Weighted Average Annual 

Growth of Human Capital (%) 
GDP Level (1960) 
Mean Years of Schooling (1960) 
Average Share of Investment (%) 
Average Share of Government 

Consumption in GDP (%) 

SLOW GROWTH ECONOMIES: 3 

Average Annual per Capita 

GDP Growth (%) 
Average Annual Growth of 

Human Capital (%) 
Weighted Average Annual 

Growth of Human Capital (%) 
GDP Level (1960) 
Mean Years of Schooling (1960) 
Average Share of Investment (%) 
Average Share of Government 

Consumption in GDP (%) 



2.58 

2.06 

7.01 

2842 
5.27 
21.3 
16.2 



0.73 

2.74 

2.85 

2372 
3.28 
17.8 
15.5 



Note: The sample is divided in thirds: 
(1). Income Growth Rate Greater Than 3.27. 
(2). Income Growth Rate Between 1.77 and 3 
(3). Income Growth Rate Less Than 1.76. 



0.46 

1.06 

3.45 

2050 
2.91 
5.53 
5.67 



0.86 

1.35 

2.84 

2022 
2.53 
7.92 
6.63 



26 



40 
schooling are greatest in moderate growth countries. This may 
mean that human capital prevents even faster convergence; that 
is, initially high levels of human capital cause high income 
countries to grow more rapidly, making it more difficult for 
initially low income countries to catch up. 
Economic Growth and Human Capital 

I now move to an econometric analysis of the data. Tables 
3-4 and 3-5 show regressions for annual growth rates of per 
capita real GDP. The results pertain to the period 1960 to 
1985 over a cross-section of 50 countries, the largest number 
of countries for which I was able to collect data on human 
capital. All the regressions are OLS until Table 3-7. 

The simplest regression is 4.1, which regresses the 
growth rate on the initial level of GDP. In this we see a 
negative effect of the initial level of GDP, which is in 
accord with convergence, but the estimated coefficient is not 
highly significant. in the remaining regressions I 
systematically add more variables. 

First I concentrate on the level and growth of human 
capital. In regression 4.2 we see that the estimated 
coefficient on initial human capital is positive and highly 
significant (B=0. 015, t = 5.07) . This result agrees with Romer 
(1990), which emphasizes that there is a permanent effect of 
the stock of human capital on the rate of growth. Notice that 
the negative effect of initial GDP is now highly significant. 
In regression 4.3 1 used weighted human capital, which is the 



41 
interaction of human capital with average real public 
educational expenditure per pupil for three decades. The 
estimated coefficient for this variable has the "correct" sign 
and is significant. 

Next, in order to measure differences in the quality of 
education across nations more carefully, I use data on 
teacher-student ratios covering the three decades. Regression 
4.4 shows that the teacher-student ratio for primary level has 
the correct positive sign and is highly significant. This 
supports the idea that the higher is the ratio the higher is 
the guality of education. The teacher-student ratio for 
secondary level has an "incorrect" sign and is significant. I 
have no explanation for this, other than to note that advanced 
countries grow more slowly, perhaps since they are not as able 
to borrow "technique" from more advanced countries, and it is 
the advanced countries that have the high secondary teacher- 
student ratios. 

Regressions 4.5 and 4.6 supplement the human capital 
variables by including "political instability" variables. With 
the other variables held constant, the estimated coefficients 
of both the initial stock of human capital and its growth 
remain positive and highly significant. The positive 
coefficient on human capital growth means that the countries 
where human capital grows at a faster rate through educational 
investments also have higher rates of economic growth. I will 



42 

Table 3-4 

Basic Regressions for per Capita GDP Growth 

(T-statistics in Parentheses) 



VARIABLE (4.1) (4.2) (4.3) (4.4) (4.5) (4.6) 



CONSTANT 0.046 0.116 0.046 0.133 0.126 0.091 

(3.75) (6.31) (3.82) (6.90) (6.25) (6.37) 

LOG(GDP60) -0.002 -0.014 -0.018 -0.016 -0.018 -0.026 

(-1.75) (-5.18) (-4.46) (-5.54) (-6.64) (-5.73) 
INITIAL HUMAN 
CAPITAL (LOG) : 

UNWEIGHTED 0.015 0.013 0.015 

(5.07) (4.31) (3.72) 

WEIGHTED 0.008 0.008 

(4.10) (3.57) 

HUMAN CAPITAL 
GROWTH: 

UNWEIGHTED 

WEIGHTED 

GDP SHARE OF : 

INVESTMENT 

GOV. CONSUMP. 



GDPDEV 



REVCOUPS 

REAL OIL PRICE 
DEVIATION: 

TEACHER STUDENT 

RATIO (1960) : 

PRIMARY 

SECONDARY 



R 2 
NOB 











0.499 
(3.09) 


0.152 
(4.71) 


- - 


- - 


- - 


- - 


0.138 
(7.73) 
-0.160 
(-6.42) 

0.440 
(4.70) 


0.107 
(5.40) 
-0.144 
(-5.39) 

0.450 
(4.83) 




— ~ 


— — 


— — 


-0.009 
(-3.62) 


-0.007 
(-3.10) 






"" 


— mm 


-0.021 
(-4.06) 


-0.021 
(-4.03) 


- - 


- - 


- - 


0.467 

(2.06) 

-0.195 

(-3.33) 


— — 


- - 


0.002 
1149 


0.02 

1149 


0.01 
1149 


0.04 
925 


0.15 
999 


0.16 
999 



43 
discuss the other variables in these regressions in detail 
later. 
Convergence 

Convergence implies that there is negative relation 
between the growth of per capita GDP and the initial level of 
per capita GDP. In other words, convergence indicates that 
poor countries can catch up to rich countries in the long run. 
The results in regressions of Tables 3-4 and 3-5 tend to 
support the idea of convergence/ 

In 4.1 I regress growth on the initial level of per 
capita GDP. The coefficient is negative, as predicted by the 
Solow model, but insignificant. When I hold constant initial 
human capital in regressions 4.2 and 4.3, I observe that the 
coefficient for initial per capita GDP remains negative but 
becomes highly significant. This result suggests that 
diminishing returns set in when initial human capital is held 
constant, but not before. The result is not supportive of the 
Solow model augmented to include human capital, (Mankiw et 
al., 1990), because it is initial human capital that is held 
constant rather than its growth. 

In regressions 4.5 and 4.6 1 add growth in human capital. 
I find evidence that both initial human capital and its growth 
contribute the growth in per capita GDP. The finding that 
initial human capital matters is most reminiscent of the Romer 
(1990) model in which comprehensive investment depends on the 
endowed skill level. That model yields parallel or even 



44 
divergent growth paths, and thus does not exhibit the 
diminishing returns feature of the Solow model. Thus the 
"convergence" result that emerges from regressions 4.3-4.6 is 
very partial in its nature. Poor countries can catch up since 
the coefficient on initial income is negative, but only if 
human capital is held constant, which is not necessarily true 
in reality. Rather, what Table 3-4 suggests is that 
convergence is weak, since rich countries have a growth 
advantage conferred by the abundance of their human skills. 
Government Policies Towards Investment and Consumption 

One of the factors affecting economic growth is the ratio 
of investment in physical capital to income. In theoretical 
models, such as Becker, Murphy, and Tamura (1990), growth of 
per capita income and the investment ratio tend to move 
together. Holding constant the other explanatory variables 
included in the regressions, these theories predict that 
growth and the investment ratio will be positively correlated. 
I examine this in regressions 4.5 and 4.6. The results for 
these regressions show that the investment ratio is positively 
related with growth and in most regressions it is highly 
significant, 0.138 with a t-ratio = 7.73 in regression 4.5. 

Barro (1990, 1991) found that the ratio of real 
government consumption to real GDP had a negative association 
with growth and investment. The argument was that government 
consumption had no direct effect on private productivity, but 
reduced saving and growth by lowering the after tax marginal 



45 
product of capital. Tables 3-4 and 3-5 do indicate a 
significantly negative relation between government consumption 
and growth; for instance, in regression 4.5 the estimated 
coefficient is -0.160, t-value = -6.42. 5 

In Table 3-6, I regress the investment ratio on the other 
variables. Countries with larger initial GDP invest less than 
others. This is especially strong once I control for human 
capital weighted by real expenditure per pupil. 

In Table 3-6, I see a positive effect of government 
consumption on investment. One explanation for this effect may 
be that government expenditure on education, which is a part 
of government consumption here, is more like public investment 
than public consumption. Thus this expenditure probably 
affects private-sector productivity, which would have a 
positive effect on private investment. 
Other Explanatory Variables 

One very important determinant of economic growth is 
population growth. Becker and Murphy (1990) and Mankiw et al. 
(1990) discuss this issue. Mankiw et al. examine a sample of 
98 countries, and find that 80 percent of the variation in 
income per capita can be explained by population growth, 
saving, and schooling. They find that higher population growth 
lowers income per capita. In Table 3-5 1 confirm this negative 
impact of population growth in economic growth; see, for 
example, regressions 5.3 and 5.5. 



46 
I have also added two variables to measure political 
instability, REVCOUPS and ASSASSINATIONS. The variable 
REVCOUPS is the number of revolutions plus coups per year and 
the variable ASSASSINATIONS is the number of assassinations 
per year. The coefficients of both variables are negative and 
highly significant in all the regressions. One possibility is 
that these variables can be viewed as threats to property, and 
hence they detract from investment and growth. 

In order to show the impact of international and civil 
wars on economic growth across countries, I use four 
variables: INTERNATIONAL AND CIVIL WAR YEARS, INTERNATIONAL 
AND CIVIL WAR CASUALTIES, MILITARY EXPENDITURE DEVIATION, and 
AVERAGE OF REAL MILITARY EXPENDITURES . I include these effects 
in regressions 5.3 to 5.6. The results for the last three 
variables are negative and significant in 5.4 and 5.5. 
However, the result for war years in regression 5.3 indicates 
that there is a highly positive relation between war years and 
economic growth. A negative impact of war on economic growth 
seems more reasonable since war has a destructive effect on 
physical capital and much income goes for military 
expenditures that may have few spillovers to the civilian 
economy. We can see this in the negative effect of war years 
on physical investment in regression 6.5. One reason for the 
positive effect of war years on growth may be that recovery 
from war is usually rapid. The idea is that a wartime 
destruction of physical capital in a country stimulates more 



47 
investment in this type of capital as well as in human 
capital. Then, per capita incomes exceed what they would have 
been had the war not happened. These results agree with the 
idea of Becker, Murphy and Tamura (1990) who pointed out: 
"Wartime destructions of physical and human capital have 
different consequences because human capital is knowledge 
embodied in people. When too much knowledge is destroyed, an 
economy loses the foundation for further accumulations of 
knowledge -whether embodied in people or disembodied in 
technologies- which is the essence of economic growth." p. S35 

Next, As in Barro (1991) I constructed dummy variables 
for countries with socialist and mixed economy systems. In 
Table 3-5 the estimated coefficients on the socialism dummy 
are negative but insignificant in regressions for growth. The 
mixed systems dummy is weaker still. The general failure of 
these variables suggests the division of economic systems into 
these groups may be subject to error, or that holding constant 
my other variables, the type of government has no effect on 
growth . 

Next, I considered two controls for business cycles. 
First, the regressions in Tables 3-4 and 3-5 show a 
significant positive relation between growth and the level of 
the country's GDP deviation from rest of the world. For 
example, the estimated coefficient in regression 5.6 is 0.455, 
t-value =4.9. Second, I also used real oil price deviations 
to capture oil price shocks. The regressions in Tables 3-4 and 



48 
3-5 show that real oil price fluctuations have a negative 
effect on economic growth across countries. For instance, in 
regression 5.5 the coefficient is -0.021 and t-ratio = -4.12. 
However, in the investment ratio regressions of Table 3-6, the 
impact of oil price deviation is positive and significant. 

Finally, countries in Africa and Latin America have 
poorer growth performance than other countries. To examine 
this, I constructed a dummy variable for Africa (which equals 
one for countries in Africa) and a dummy variable in Latin 
America (which equals one for countries in South and Central 
America) . Using these dummies and the other explanatory 
variables, I found insignificant coefficients for these 
dummies. This means that there is no country effect for Africa 
and Latin America with respect to economic growth. However, in 
Table 3-6, these variable are positive and significant on 
investment regressions. A comparison of regressions in Table 
3-5, shows the effect of inclusion of these continent dummies 
on the estimated coefficient of GDP60 and human capital 
variables is not trivial. For example, for initial weighted 
human capital, inclusion of these dummies reduces the 
coefficient from 0.007 (t-value = 3.02) to 0.005 (t-value = 
1.86) . 6 

Simultaneous Equations Analysis of Income Growth and Human 
Capital Grow th Using the 2SLS Method 

In Table 3-7, I present two stage least squares estimates 
of regressions for economic growth and human capital growth. 



49 
The difference between regressions 7.1 and 7.2 versus 7.3 and 
7.4 is that in the first two regressions I use unweighted 
human capital measures, while I use weighted ones in the last 
two regressions. 

Reassuringly, the 2SLS estimates of the parameters are 
almost the same as OLS estimates presented in Tables 3-4 and 
3-5. The key difference between these two sets of 2SLS 
estimates lies in the statistical insignificance of the 
positive effect of the investment ratio in regression 7.3. 
Consider regressions 7.1 and 7.3. The estimated coefficients 
for human capital and its subsequent growth are more 
significant when I use weighted human capital, but the 
significance of the negative effect of GDP60 is somewhat 
lower. One explanation for this might be that the higher the 
human capital and its growth in quality of education, the 
faster a country can catch up to the more advanced countries. 
Government consumption also has a negative impact on economic 
growth, -0.136 with t-ratio = -3.95 in regression 7.1. 

Considering the regressions for human capital growth, 
equation 7.2 shows that the higher the initial human capital 
the lower is its growth, -0.025 with t-ratio = -20.4. The 
regression with the weighted measure, 7.4, gives similar 
results. Investment again has positive and significant effects 
on economic growth and human capital, particularly in 7.4. 






50 
Interpretation of the Findings 
Table 3-8 collects the major findings of this paper by 
reporting the imputed impact of the variables at their means. 
The first part of the Table shows the calculated effects using 
the OLS estimates from regressions 4.5, 4.6, 5.1, and 5.3. The 
second part displays results using the two stage least squares 
estimates from regressions 7.1 and 7.3. 

The first part confirms the partial convergence finding: 
For example, in regression 4.6 in Table 3-8, the highly 
negative effect of initial GDP on growth is 19%. Government 
consumption has also a negative and highly significant effect 
on growth, for instance 1.8% in regression 4.5. Also, 
regressions 5.1 and 5.3 on Table 3-8 indicate that population 
growth lowers the growth across countries, though by not as 
much as the above variables. However, human capital and 
investment, which are both highly significant, increase 
economic growth. Consider regression 4.6. Here, the effects on 
growth of weighted initial human capital, growth of human 
capital, and investment are 11%, 0.9%, 2.3%, and are all 
highly significant. Basically, investment in both physical and 
human capital has a large positive impact on growth across 
countries, though the effect of investment in human capital 
appears with a lag of 10 or 2 years. 

The second part of Table summarizes imputations from the 
2SLS regressions. The effects of variables, save for 
investment, are the same as above. Indeed the only difference 



51 
between the two lies in the statistical insignificance of the 
investment ratio in the second part. Consider regression 7.1: 
Here the effects on growth of initial GDP, initial human 
capital, growth of human capital, investment ratio, and 
government consumption are -12.1%, 2.1%, 2.1%, 3.5%, and 
-1.4%, respectively. Interestingly, in regression 7.3 the 
effect of the initial level of GDP is almost precisely 
canceled by the effects from the initial and growth rate of 
human capital. This suggests that the poor nations can catch 
up to rich nations only if they invest more in human capital. 
Next I compare how the contribution to growth of the 
above variables differs between the poor and rich group 
countries. Looking again at means and using regression 4 . 5 as 
my example, initial GDP lowers growth by 15% in rich countries 
compared to 11% in poor countries. In addition, the positive 
contributions of unweighted initial human capital and its 
subsequent growth are 2.8% and 0.8% in rich nations compared 
to 0.9% and 1.9% in poor nations (to compare the imputed 
effects of weighted human capital and its growth I can use 
regression 4.6, where the impacts are 12.9% and 0.8% in rich 
and 9.9% and 1% in poor nations). The effects of investment 
and government consumption are 3.5% and -2.2% in rich and 2.3% 
and -2.8% in poor countries. Finally, in regression 5.3, the 
negative contribution of population growth to mean economic 
growth is 0.4% in rich and 0.9% in poor countries. 






52 

Turning to 2SLS results, in regression 7.1, initial GDP 
lowers the growth by 13.5% in rich and 10.5% in poor nations. 
The positive contributions of unweighted human capital and its 
growth are 3.2% and 1.4% in rich and 1% and 3.3% in poor 
countries. In addition, the contributions of investment and 
government consumption to mean economic growth are 4.2% and 
-1.9% in rich nations and 2.8% and -2.4% in poor nations. In 
regression 7.3, the imputed effects of weighted human capital 
and its growth are 30.6% and 2.1% in rich and 23.8% and 2.7% 
in poor nations. 

The patterns are by now familiar: the negative effect of 
initial GDP on growth is the largest, followed by government 
consumption and population growth. Initial human capital and 
its subseguent growth, and investment in physical capital have 
positive and significant impacts on growth. 

Conclusion 

Building on the theory of economic growth this study 
contributes to the empirical evidence about the linkages 
between growth, investment, and human capital . The correlation 
between per capita growth and the initial (1960) level of per 
capita GDP is substantially negative when human capital 
(proxied by mean years of schooling) is held constant. This 
suggests convergence. However, given the level of initial per 
capita GDP, the growth rate is positively related to initial 
human capital and growth of human capital. Thus, the poor 
countries tend to catch up with rich countries if the poor 



53 
countries have high initial human capital per person (in 
relation to their level of per capita GDP) or have faster 
growth in their human capital per person, but not otherwise. 
Also, countries with high human capital have low population 
growth and high ratios of physical investment to GDP. 

Per capita GDP growth is negatively related to government 
consumption and positively related to the ratio of investment 
to GDP. An explanation for this can be that government 
consumption introduces distortions, such as high taxation, 
while investment in capital stimulate growth. However, since 
government consumption seems to lead to increased growth in 
investment, through this indirect avenue government 
consumption is positively related to economic growth. 

Political instability (proxied by revolutions plus coups) 
is negatively related to growth. This is probably because of 
their negative impact on investment. 

Notes 

1. As Barro indicates: "It would be better to use proxies for 
the initial stock of human capital per person rather than 
variables that relate to the flow of investment in human 
capital. The stock of human capital derived from formal 
education depends on current and lagged values of school- 
enrolment rates." Barro (1991), p. 414. Therefore, it seems to 
me that in this paper I have a proper proxy for human capital 
and its growth. 

2. This measure of the stock of human capital per person is 
the average years of schooling in the population and is based 
on UNESCO data that in turn are derived from the various 
national censuses. The processing of the data to derive this 
statistic is nontrivial; See the Appendix B for details. 

3. Two possible explanations immediately come to mind why 
these low income countries, which have the highest growth rate 



54 



of human capital, do not have the highest growth of income: 
the high degree of political instability in these countries 
and the extremely high growth of population in these nations. 
I examine this more rigorously later. 

4. In tests of convergence, there are at least two pitfalls. 
First, one should not select the sample on the basis of latter 
success, since this tends to give convergence. This problem 
plagues the sample of countries in Maddison (1982) , which were 
employed by Baumol (1986) to test for convergence. De Long 
(1988) severely criticized this selection bias. The second 
problem, which also was pointed out by De Long, is that errors 
in initial GDP are accompanied by equal and opposite error in 
growth. The result is a bias toward -1 in the estimated 
coefficient for initial GDP. To avoid these problems (biased 
sample and measurement error) , I have used the growth rate 
starting from 1961 instead of 1960 in the regressions reported 
here. I also used 1970 as the starting point of growth rate 
and obtained the same results, as reported in this paper. In 
addition, the sample which has been chosen in this paper 
contains a broad sample of nations from 5 continents and both 
poor and rich countries. As a result, it seems that De Long's 
criticisms may not have much force. 

5. I also employed the government consumption variable from 
Barro's (1991) data set, which is the average from 1970 to 
1985 of the ratio of real government consumption (exclusive of 
defence and education) to real GDP, and got the same result. 

6. In addition to the UNESCO data for calculation of proxy for 
human capital, I employed data from Horn and Arriagada (1986) 
which gave me the human capital of young generations. Then I 
repeated a few regressions with this data and got the same 
signs for major variables as in the regressions with UNESCO 
data. However, the estimated coefficients and t-ratios were a 
little lower, which may be because of the lower number of 
observations in the later data set. Another reason can be that 
the younger human capital is not yet completely involved in 
production process. 



55 

Table 3-5 
Regressions for per Capita GDP Growth 
Experiments with War Effects, Dummy Variables, 
Population Growth 
(T-statistics in Parentheses) 



and 



VARIABLE 


(5.1) 


(5.2) 


(5.3) 


(5.4) 


(5.5) 


(5.6) 


CONSTANT 


0.116 


0.119 


0.114 


0.112 


0.112 


0.114 




(6.54) 


(6.60) 


(6.44) 


(6.32) 


(6.36) 


(6.43) 


LOG(GDP60) 


-0.024 


-0.023 


-0.026 


-0.027 


-0.027 


-0.028 




(-4.75) 


(-4.45) 


(-5.72) 


(-5.95) 


(-5.86) 


(-5.97) 


INITIAL HUMAN 














CAPITAL (LOG) 














(WEIGHTED) 


0.006 


0.005 


0.007 


0.008 


0.007 


0.008 




(2.24) 


(1.86) 


(3.02) 


(3.32) 


(3.21) 


(3.39) 


HUMAN CAPITAL 














GROWTH 














(WEIGHTED) 


0.120 


0.110 


0.135 


0.143 


0.140 


0.142 




(3.33) 


(2.94) 


(4.14) 


(4.37) 


(4.30) 


(4.36) 



GDP SHARE OF 



INVESTMENT 


0. 




(5. 


GOV. CONSUMP. 


-0. 




(-4. 


GDPDEV 


0. 




(4. 


REVCOUPS 


-0. 




(-3. 


REAL OIL PRICE 




DEVIATION 


-0. 




(-4. 


POPULATION 




GROWTH 


-0. 




(-1- 



CONTINENTAL DUM. 
LAT. AMERICA 

AFRICA 



111 0.120 0.108 0.105 0.106 

51) (5.64) (5.44) (5.29) (5.37) 

132 -0.127 -0.138 -0.143 -0.142 

82) (-4.61) (-5.18) (-5.39) (-5.34) 

450 0.447 0.446 0.454 0.457 

84) (4.81) (4.81) (4.88) (4.91) 

007 -0.007 -0.007 -0.007 -0.007 

00) (-2.99) (-3.13) (-2.93) (-2.92) 

021 -0.021 -0.020 -0.021 -0.021 

04) (-4.05) (-4.02) (-4.07) (-4.12) 

218 -0.213 -0.339 -0.276 -0.295 

41) (-1.38) (-2.35) (-1.90) (-2.05) 



-0.004 -0.005 

(-1.35) (-1.47) 

-0.006 -0.005 

(-1.32) (-1.02) 








.108 




(5 


.42) 




-0 


.150 


( 


-5, 


.45) 




0, 


.455 




(4. 


,90) 




-0. 


,007 


( 


-2. 


,97) 




-0. 


021 


( 


-4. 


10) 




-0. 


255 


( 


-1. 


72) 



56 



Table 3-5 — continued 



VARIABLE 



COUNTRY DUMMY : 
SOCIAL 

MIXED 

INTERNATIONAL 
AND CIVIL WAR 
YEAR (DUMMY) 

INTERNATIONAL 
AND CIVIL WAR 
CASUALTIES PER 
10000 POP. 

MILITARY EXPEND. 
DEVIATION 



(5.1) (5.2) (5.3) (5.4) (5.5) (5.6) 



-0.016 
(-1.32) 

-0.0009 
(-0.36) 



0.010 
(2.06) 



-0.004 
(-1.61) 



-0.550 -0.559 
(-1.71) (-1.73) 



AVERAGE OF REAL 














MILITARY EXPEN. 












-0.037 
(-1.16) 


R 2 


0.17 


0.17 


0.17 


0.17 


0.17 


0.17 


NOB 


999 


999 


999 


999 


999 


999 



57 



VARIABLE 



CONSTANT 



LOG(GDP60) 

INITIAL HUMAN 
CAPITAL (LOG) : 



Table 3-6 
Regressions for Investment Ratios 
(T-statistics in Parentheses) 



(6.1) 



(6.2) (6.3) (6.4) (6.5) 



(6.6) 



0.146 -0.137 -0.165 -0.177 -0.134 -0.135 

(4.09) (-6.15) (-7.06) (-7.92) (-6.03) (-6.08) 

-0.011 -0.085 -0.087 -0.091 -0.086 -0.085 

(-2.27) (-12.4) (-11.6) (-12.7) (-12.4) (-12.3) 



UNWEIGHTED 


0.086 
(12.2) 












WEIGHTED 


- - 


0.067 


0.070 


0.072 


0.067 


0.066 


HUMAN CAPITAL 




(20.6) 


(18.7) 


(20.3) 


(20.7) 


(20.5) 


GROWTH : 














UNWEIGHTED 


1.164 
(4.09) 












WEIGHTED 


- - 


0.559 


0.597 


0.661 


0.562 


0.542 


GDP SHARE OF 




(11.6) 


(11.1) 


(12.7) 


(11.6) 


(11-1) 


GOV. CONSUMP. 


0.211 


0.132 


0.085 


0.020 


0.128 


0.127 




(4.80) 


(3.13) 


(1.97) 


(0.48) 


(3.05) 


(3.01) 


GDPDEV 


0.346 


0.334 


0.325 


0.320 


0.336 


0.355 




(2.08) 


(2.26) 


(2.22) 


(2.31) 


(2.27) 


(2.40) 


REVCOUPS 


-0.006 


0.0009 


0.001 


0.001 


0.001 


_ — 




(-1.45) 


(0.24) 


(0.46) 


(0.33) 


(0.31) 




ASSASSINATIONS 












-0.002 


REAL OIL PRICE 












(-2.20) 


DEVIATION 


0.015 


0.014 


0.013 


0.013 


0.014 


0.015 




(1.67) 


(1.77) 


(1.69) 


(1.72) 


(1.74) 


(1.81) 


CONTINENTAL DUM. 


: 












LAT. AMERICA 


- - 


- - 


0.009 
(1.78) 


0.013 
(2.66) 


- - 


- - 


AFRICA 


— — 


— — 


0.032 
(4.37) 


0.012 
(1.69) 


- - 


- - 





58 












Table 3-6 — 


continued 






VARIABLE 


(6.1) (6.2) 


(6.3) 


(6.4) 


(6.5) 


(6.6) 


COUNTRY DUMMY : 






SOCIAL 






0.196 
(10.8) 


- - 


- - 


MIXED 


_ _ _ _ 


— — 


0.005 


_ _ 


_ _ 


INTERNATIONAL 






(1.42) 






AND CIVIL WAR 












YEAR (DUMMY) 


— — ^ ^ 


"~ ■■ 


— — 


-0.012 
(-1.45) 


— — 


R 2 


0.31 0.45 


0.46 


0.52 


0.45 


0.46 


NOB 


999 999 


999 


999 


999 


999 









59 



Table 3-7 
Two Stage Least Squares Estimates 
Regressions for per Capita GDP Growth and 
Weighted Human Capital Growth 
(Asymptotic T-statistics in Parentheses) 



REGRESSION 


(7.1) 


(7.2) 


(7.3) 


(7.4) 


DEPENDENT PER CAPITA 


HUMAN CAPITAL 


PER CAPITA 


HUMAN CAPITAL 


VARIABLE GDP GROWTH 


GROWTH 


GDP GROWTH 


GROWTH 


CONSTANT 


0.100 


0.020 


0.051 


0.155 




(4.16) 


(3.41) 


(1.86) 


(6.77) 


LOG(GDP60) 


-0.016 


0.002 


-0.041 


0.058 


INITIAL HUMAN 


(-5.64) 


(2.86) 


(-4.58) 


(11.3) 


CAPITAL (LOG) : 










UNWEIGHTED 


0.017 


-0.025 


- - 






(2.60) 


(-20.4) 






WEIGHTED 


- - 


- - 


0.019 


-0.042 


HUMAN CAPITAL 






(3.14) 


(-16.3) 


GROWTH : 










UNWEIGHTED 


0.862 
(2.71) 


- - 


- - 


- - 


WEIGHTED 


- - 


— — 


0.393 


^ 


PER CAPITA GDP 






(3.08) 




GROWTH 


- - 


-0.004 


- - 


0.349 


GDP SHARE OF : 




(-0.13) 




(2.53) 


INVESTMENT 


0.168 


0.012 


0.052 


0.225 




(7.17) 


(1.72) 


(1.12) 


(7.11) 


GOV. CONSUMP. 


-0.136 


— _ 


-0.141 






(-3.95) 




(-3.74) 




GDPDEV 


0.185 
(1.63) 


- - 


0.192 
(1.65) 





REVCOUPS 


-0.009 


— — 


-0.007 




REAL OIL PRICE 


(-4.03) 




(-3.24) 




DEVIATION 


-0.018 
(-3.49) 


^ ^ 


-0.016 
(-2.95) 


- - 






60 
Table 3-7 — continued 



REGRESSION (7.1) (7.2) (7.3) (7.4) 

DEPENDENT PER CAPITA HUMAN CAPITAL PER CAPITA HUMAN CAPITAL 
VARIABLE GDP GROWTH GROWTH GDP GROWTH GROWTH 



CONTINENTAL 


DUM. 










LAT. AMERICA 


-0.001 


-0.003 


0.010 


-0.032 






(-0.51) 


(-3.71) 


(1.75) 


(-8.72) 


AFRICA 




0.002 


0.015 


0.003 


0.039 






(0.27) 


(7.40) 


(0.39) 


(4.33) 



INTERNATIONAL 
AND CIVIL WAR 
CASUALTIES PER 

10000 POP. -0.005 0.0008 -0.008 0.005 

(-1.39) (1.42) (-1.86) (2.00) 

MILITARY EXPEND. -0.062 - - 0.150 - - 

DEVIATION (-0.13) (0^32) 

TEACHER STUDENT 
RATIO (1960) : 



PRIMARY - - -0.232 



SECONDARY -0.008 



WORK FORCE IN 

INDUSTRY - - -0.027 



URBANIZATION -0.007 



% LITERATE 0.044 



-0.348 



(-4.22) (-1.34) 



-0.031 



(-0.60) (-0.49) 



- - -0.048 

(-4.52) (-1.83) 



-0.042 



(-3-37) (-4.28) 



0.080 



(11-5) (6.11) 

NOB 630 630 629 629 



61 

Table 3-8 

Estimated Mean Contributions to Economic Growth 

(T-statistics in Parentheses) 







(OLS) 






(2SLS) 


VARIABLE 


REG. 
(4.5) 


REG. 
(4.6) 


REG. 
(5.1) 


REG. 
(5.3) 


REG. 
(7.1) 


REG. 
(7.3) 


LOG(GDP60) 


-0.135 


-0.195 


-0.180 


-0.195 


-0.121 


-0.310 



INITIAL HUMAN 
CAPITAL (LOG) : 



(-6.64) (-5.73) (-4.75) (-5.72) (-5.64) (-4.58) 



UNWEIGHTED 0.018 -- -- -- 0.021 

(3.72) (2.60) 

WEIGHTED 0.112 0.084 0.099 0.269 

(3.57) (2.24) (3.02) (3.14) 

HUMAN CAPITAL 
GROWTH: 



0.021 


- - 


(2.71) 




— — 


0.026 




(3.08) 



UNWEIGHTED 0.013 - - - - - - 

(3.09) 

WEIGHTED - - 0.009 0.007 0.008 

(4.71) (3.33) (4.14) 
GDP SHARE OF : 

INVESTMENT 0.030 0.023 0.024 0.024 0.035 0.010 

(7.73) (5.40) (5.51) (5.44) (7.17) (1.12) 

GOV. CONSUMP. -0.018 -0.016 -0.015 -0.015 -0.014 -0.015 

(-6.42) (-5.39) (-4.82) (-5.18) (-3.95) (-3.74) 

POPULATION 

GROWTH - - - - -0.004-0.006 

(-1.41) (-2.35) 



NOTE. Estimated contributions are regression coefficients times 
means of variables. 



CHAPTER 4 

TECHNOLOGY CREATION, TECHNOLOGY TRANSFER, AND 

ECONOMIC GROWTH 

Introduction 

Countries differ in their economic performance, and these 
differences persist for long periods. Countries also differ in 
the technologies they use. These two facts are related in some 
undiscovered way. 

The idea of this chapter is to study technology creation, 
technology transfer, and their relative contributions to 
countries classified by level of technology. Technology 
indicators here capture the effect of original innovations as 
well as imitation and adaptations through technology transfer 
and licensing. 

We have already indicated that there are three main 
concerns in this chapter. The first is with the connection 
between home-based resident, or original technology and 
economic growth. This is impounded in the residual term in a 
model of disembodied growth of the kind advanced by Solow 
(1957) . 

My second concern is with the effect of borrowed 
technology on the growth of less advanced countries. The 
recent literature concludes that many newly industrialized 
countries (NICs) have enjoyed very high growth rates at the 

62 



63 
expense of leader countries. Also, the evidence in Evenson 
(1984) suggests that imitation by less industrialized 
countries of technologies developed in more advanced countries 
is an important source of growth in the less developed 
countries. A related point is that original research in 
advanced countries can be imported by LDCs, thereby permitting 
adaptive invention rather than expensive original research. To 
examine the empirical significance of these ideas, this 
chapter develops measures of imitation and adaptation. 
Therefore, I study the link between technological progress and 
skills at adapting new technologies. The ability to adapt 
technology is correlated with measurable factors such as 
patents and the stock of scientists and engineers (S&E) . 

My third concern is with the potential for enhanced 
division of labor, through interrelated growth of advanced and 
follower countries, and for world redistribution of output as 
NICs increase their technology acquisition at the cost of 
market share in the advanced countries. It is possible that 
the new international division of labor and the world 
redistribution of output through this process could be a 
reason for the slowdown of advanced countries since 1973. 

I turn next to a review of the literature. Obviously, 
while there is an enormous literature on growth and 
technology, this chapter will concentrate on a few key 
articles. 






64 
The starting idea for this chapter began with Evenson 
(1984) . Using data on patented inventions from many countries, 
he reaches two principal conclusions: First, the data strongly 
support the notion of comparative advantage of advanced 
countries in invention. The production of original invention 
is concentrated in certain firms located in countries with the 
best laboratories. Industry in "follower" countries imports 
inventions and concentrate on adaptive invention rather than 
investing heavily in R&D. Second, the data show that 
inventions per scientist and engineer have declined from the 
late 1960s to 1970s in almost all of the fifty countries for 
which data are available. Evenson declared that the declining 
trend supports the case for interpreting this phenomenon as 
the result of exhaustion of "invention potential." However, 
Griliches (1990) points to a declining propensity to patent 
instead. 

In the same survey paper Griliches points out that in 
spite of all difficulties, patent statistics remain a unique 
resource for the analysis of the process of technical change. 
Also, he believes that nothing else comes close in the 
quantity of available data, accessibility, and the potential 
industrial, organizational, and technological detail. This 
viewpoint is maintained in the present chapter. 

Jovanovic and Lach (1990) suggest that variation in GNP 
over countries arises because countries differ in the speed of 
implementation of new technology. Their model implies that 



65 
slow countries end up with lower GNP. They predict slower and 
more trended growth in the laggard countries. They also 
suggest that diffusion lags should not affect long-run growth. 

Grossman and Helpman (1991) also see the variation in 
growth rates as the key fact to be explained. They point out 
that a reading of recent economic history suggests two 
important trends. First, technological innovations are an ever 
more important contributor to growth. Second, the economies of 
the world are increasingly open and interdependent. The two 
trends are related. Grossman and Helpman suggest that rapid 
communication and close contacts among innovators in different 
countries facilitate invention and the spread of new ideas. 
Also, they emphasize that the rapid changes in technology 
promote trade and integration into the world economy. 

Grossman and Helpman model the diffusion of technology as 
the central activity in innovation and growth. They begin with 
the point that technical changes has often been treated as an 
exogenous process in long-run economic analysis. This would be 
appropriate if advances in technology followed automatically 
from fundamental scientific discoveries and if basic research 
were independent of market forces. While obviously wrong from 
a global perspective, they explore this perspective from the 
standpoint of developing economies, for whom technical change 
would be largely exogenous if knowledge diffused inevitably 
from the industrialized North to the lagging South and if the 



66 
pace of innovation in the North were little affected by events 
in the South. 

Finally, Krugman (1979) in his famous model of product 
cycle divided countries into innovating North and 
noninnovating South. Innovation consists of the development of 
new products, which can be produced at first only in North, 
but eventually the technology of production transfers to 
South. This technological lag gives rise to trade, with North 
exporting new products and importing old products. 

The present chapter is closely tied to the developments 
summarized above. I use pooled time-series cross-section data 
to explore issues of technology creation and transfer. To 
control for transitional effects I have employed the same 
control variables used in a previous chapter. Due to data 
limitations the sample consists of fifty-four countries during 
the period 1968-1985. 

I employed two sets of proxies for created versus 
borrowed technology. The first is resident patents, which are 
collected from industrial property statistics (World 
Intellectual Property Organization, various years) . This 
measures in broad outline the new research output of a 
country. These are annual data for the period 1968-1985. The 
second proxy is nonresident patents, which are also collected 
from the same source. This is a proxy for technology licensing 
and perhaps transfer. In addition, I have collected data on 



67 
research scientists and engineers from the UNESCO Statistical 
Yearbooks (various years) . 

The reminder of this chapter is arranged as follows. The 
second section describes the data set. Empirical results are 
reported in the third section. I find that technology strongly 
contributes to growth, but depending on the country — advanced 
or nonadvanced — the form of the technology effect is 
different. For example, in the case of advanced countries a 
strongest contribution comes from original inventions. On the 
other hand, in nonadvanced countries the adoption of 
technology is more important. Indeed, in the NICs the 
imitation of technology devised in the lead countries is the 
main cause of their rapid growth. It is obvious that a corps 
of research scientists and engineers is very crucial to this 
process. In the rapidly advancing Pacific Rim countries the 
transfer of western technology is an especially large 
contributor to growth. The last section of this chapter 
explores directions for continued research. 

Description of the Data 
The study uses 1968-1985 real GDP data, its components- 
investment and government consumption—and population data are 
again from Summers and Heston (1988) . The measure of growth is 
the log first difference of real per capita GDP. Investment 
and government consumption are expressed as ratios to GDP and 
serve once more as controls. 



68 

As already noted, main technology variables include 
resident patents and nonresident patents. Resident patents 
measure the number of patents granted to nationals; 
nonresident patents are patents granted to foreigners by the 
domestic country. 

Technology efficiency is the ratio of inventive output 
per unit input of R&D personnel. In particular, technology 
efficiency is the ratio of patents by residents of a country 
divided by research scientists and engineers in the same 
country. 

Table 4-1 provides an overview of the data. The data are 
reported at 4 or 5 year intervals to provide a sense of 
change. The full sample, which contains 54 countries, has been 
divided into three groups: newly industrialized, advanced, and 
rest of the world countries. Advanced countries are basically 
high income western countries. For the definition of these 
three groups of countries see the notes to Table 4-1. Together 
these countries are the only ones for whom data on patents and 
scientists and engineers are reported and thus comprise the 
set of active participants in technology. 

In the NICs the number of patents by residents rises 
markedly, but the number of nonresident patents decreases 
slightly. Since nonresident patenting is licensing of 
technology from elsewhere, this suggests a decline in 
international protection of intellectual property. In other 
words some nations use other countries' technology without 






69 

Table 4-1 
Descriptive Statistics 



Resident 
Patents 
per 
S & E 



Newly Industrialized 
Countries (NICs) 1 



Resident 
Patents 



Non- 
Resident 
Patents 



Rest of 
the World 
Resident 
Patents 



1968-1972 


0.097 


2883 


3073 


128551 


1973-1976 


0.071 


3984 


2960 


126483 


1977-1981 


0.043 


4646 


2970 


114094 


1982-1985 


0.031 


5300 


2726 


117417 


Advanced Countries 2 










1968-1972 


0.160 


6728 


13015 


124706 


1973-1976 


0.113 


6068 


11513 


124399 


1977-1981 


0.081 


4880 


9521 


113860 


1982-1985 


0.060 


4761 


9164 


117956 


Rest of the World 3 










1968-1972 


0.036 


98 


540 


131336 


1973-1976 


0.034 


81 


420 


130386 


1977-1981 


0.027 


70 


355 


118670 


1982-1985 


0.024 


50 


289 


122667 



Notes: 

1. These countries include Korea, Japan, Philippines, Singapore, 
Israel, Greece, Portugal, Spain, Turkey, Mexico, and Brazil. 

2. These are Australia, Austria, Belgium, Denmark, Finland, France, 
Germany, Italy, Netherlands, Norway, Sweden, Switzerland, UK 
Canada, USA. ' 

3. Rest of the world countries are Algeria, Egypt, Kenya 
Mauritius, Morocco, Tunisia, Uganda, Zaire, Zambia, Zimbabwe , 
India, Iran, Iraq, Jordan, Sri-Lanka, Cyprus, Iceland, Ireland, 
Malta, El-Salvador, Guatemala, Argentina, Bolivia, Chile, Colombia 
Ecuador, Uruguay, Venezuela. 



70 
paying licensing fees. Another less likely possibility is that 
these countries now depend on their own inventions and are 
less dependent on foreign technology. 

The efficiency of the NICs has also declined over time. 
This is in line with Evenson's finding that inventions per 
scientist and engineer have declined in almost all countries 
for which data are available. 

Now turn to the data for the advanced countries. A steep 
decline in the number of both resident and nonresident patents 
is readily apparent. Clearly the lead of the advanced 
countries has diminished with time. Again, in these countries 
efficiency rate has declined markedly. But there is also a 
decline of the advanced countries relative to the Pacific Rim, 
principally Japan. Finally, a large number of resident patents 
for rest of the world betokens a large pool of the stock of 
new technology available to country. This is very likely a 
proxy for the externality or spillover effect of technology. 
Table 4-2 reports mean and standard deviation for the 
various technology indicators by our three groups of 
countries. As expected the average number of scientists and 
engineers engaged in R&D is much larger in advanced nations. 
The indicators for NICs are greater than in other countries 
but less so. We see that the ability to absorb new technology 
and thus to grow has risen in these nations. Average numbers 
of resident and nonresident patents in advanced countries are 
higher than in the NICs and other countries. Together these 



71 

Table 4-2 

Descriptive Statistics (1968-85) 



Variable Mean Standard 

Deviation 



NEWLY INDUSTRIALIZED COUNTRIES: 

Annual per Capita GDP Growth (%) 3.96 4.62 

GDP Level (1960) 1627 633 

Resident Patents 8 per Million Population 53 92 

Nonresident Patents b per Million Population 123 136 

S&E C per Million Population 653 1066 

Annual Growth of Resident Patents per S&E (%) -6.7 44.9 

Annual Growth of Nonresident Patents per S&E (%) -4.7 79.3 

ADVANCED COUNTRIES: 

Annual per Capita GDP Growth (%) 2.55 2 53 

GDP Level (1960) 5069 1046 

Resident Patents per Million Population 137 129 

Nonresident Patents per Million Population 551 447 

S&E per Million Population 1492 575 

Annual Growth of Resident Patents per S&E (%) -6.0 42.0 

Annual Growth of Nonresident Patents per S&E (%) -7.4 45.0 

REST OF THE WORLD: 

Annual per Capita GDP Growth (%) 

GDP Level (1960) 

Resident Patents per Million Population 

Nonresident Patents per Million Population 

S&E per Million Population 

Annual Growth of Resident Patents per S&E (%) 

Annual Growth of Nonresident Patents per S&E ('■ 



1.50 


7.32 


1575 


1258 


5 


10 


45 


71 


118 


279 


-3.9 


66 


-6.0 


101 



Notes: 

a. Resident Patents: Grants of patents to residents. 

b. Nonresident Patents: Grants of patents to nonresidents. 

c. S&E: Number of scientists and engineers engaged in R&D. 



72 
trends suggest movement of high technology production outside 
the lead countries, but still a sharp difference in 
comparative advantage of advanced countries in technology. 
Finally, the average annual growth of patents per scientist 
and engineer is everywhere negative. 

Empirical Results 
Simple Correlations 

The data are not especially familiar. Thus, Table 4-3 

reports simple correlations between income growth and the 

different technology variables for the full sample, advanced, 

and follower countries. In the first panel, the advanced 

nations' resident patents and rest of the world's resident 

patents are incorrelated and positively correlated with growth 

respectively. In addition, there is a positive correlation 

between income growth in advanced countries and the average 

efficiency ratio (inventions per scientist and engineer) in 

the NICs. Resident and nonresident patents are highly 

correlated, suggesting that home technology is associated with 

licensing. The same applies to rest of the world's resident 

patents and resident patents in advanced countries. This 

suggests correlated increases in invention, perhaps promoted 

by exchange of information. 

I now turn to the full sample. The only positive and 
significant correlation is between the rest of the world's 
resident patents and income growth. In the follower countries' 



73 

Table 4-3 

Simple Correlation Matrix 

(P Values in the Second Line for each Variable) 

Avg. 
Income Res. Non- Res. Rest of Avg. Res. 
Growth Patent res. Patent World Patent Patent 
Patent per Res. per per 
S&E Patents S&E S&E 

NICs NICs 



1. Advanced Countries: 

Income 1.000 -0.050 0.034 0.118 0.210 0.296 0.255 

Growth 0.0000 0.4071 0.5724 0.0516 0.0005 0.0001 0.0001 

Res. Patents 1.000 0.529 0.015 0.740 0.069 0.059 

0.0000 0.0001 0.7945 0.0001 0.2543 0.3320 

Nonresident 1.000 0.158 -0.315 0.160 0.129 

Patents 0.0000 0.0090 0.0001 0.0082 0.0340 

Res. Patents 1.000 0.152 0.332 0.284 

per S&E 0.0000 0.0123 0.0001 0.0001 

Rest of the 1.000 0.288 0.264 

World Res. 0.0000 0.0001 0.0001 

Patents 
Avg. Patents 1.000 0.930 

per S&E in 0.0000 0.0001 

NICs 
Avg. Res. 1.000 

Patents 0.0000 

per S&E 

in NICs 

2. All Countries: 

Income 1.000 0.027 0.032 -0.032 0.125 

Growth 0.0000 0.3937 0.3132 0.3054 0.0001 

Res. Patents 1.000 0.557 -0.060 -0.632 

0.0000 0.0001 0.0607 0.0001 

Nonresident 1.000 -0.052 -0.316 

Patents 0.0000 0.0996 0.0001 

Res. Patents 1.000 0.03 

per S&E 0.0000 0.3485 

Rest of the 1.000 

World Res. 0.0000 

Patents 



74 
Table 4-3 — continued 



Avg. 
Income Res. Non- Res. Rest of Avg. Res. 
Growth Patent res. Patent World Patent Patent 
Patent per Res. per per 
S&E Patents S&E S&E 

NICs NICs 



3. Follower Countries 3 : 

Income 1.000 0.047 0.034 -0.029 0.134 

Growth 0.0000 0.2113 0.3577 0.4282 0.0003 

Res. Patents 1.000 0.635 -0.032 -0.530 

0.0000 0.0001 0.3887 0.0001 

Nonresident 1.000 0.166 -0.323 

Patents 0.0000 0.0001 0.0001 

Res. Patents 1.000 0.005 

per S&E 0.0000 0.8915 

Rest of the 1.000 

World Res. 0.0000 
Patents 



Note: 

a. The follower countries are all the countries except the advanced 
countries. See the notes to Table 4-1. 






75 
sample, the correlation between income growth and rest of the 
world's resident patents is even stronger. 

Generally these correlations indicate that there is a 
chain of technology exchange among countries. First, the 
advanced countries exchange home generated inventions with 
each other and thereby benefit from each other. Also, new 
technology benefits middle income countries and poor 
countries, in their role as recipients of invention. 
Full Sample Regression Results 

Table 4-4 presents regression findings from the pooled 
data. The dependent variable is the annual rate of growth of 
per capita GDP in 54 countries, the largest number of 
countries for which I was able to collect clean data on 
patents. The period is 1968-1985. 

I begin with simple patent variables and their 
relationship to growth. In regression 4.1 I find that the 
estimated coefficient on resident patents is positive and 
significant, 0,062 with a t of 2.2. Since this represents 
technology creation, this result suggests that new inventions 
have a strong effect on economic growth. The assumption is 
that patents are a good proxy for the output of inventive 
activities. Turning to rest of the world's resident patents, 
again the estimated coefficient is positive and even more 
strongly significant, 0.111 with a t-ratio of 6.10. Very 
likely access to inventions elsewhere advances the growth of 
other countries through imitation. In other words, this result 






76 
suggests that borrowing of technology is an important factor 
for growth. 

Regression 4.2 is an alternative specification of 
technology transfer. As expected, the coefficient on non- 
resident patents is positive and significant. This result is 
consistent with the idea that technology licensing is an 
important engine of growth. Therefore, developing countries 
are able to imitate or adapt the technologies that have been 
created in advanced countries. This result is in line with 
much research in this area, especially work by trade 
economists. Vernon (1966) first described technology transfer 
in his celebrated "product cycle" hypothesis, which led to 
numerous theoretical and empirical studies. Results in the 
remaining regressions in Table 4-4 confirm the importance of 
technology transfer from developed to less developed countries 
for the pattern of relative incomes. In regression 4.3, I have 
rescaled nonresident patents by GDP level of each country to 
incorporate size of each country's economy. Nonresident 
patents continue to be positive and significant as expected. 
However, in regression 4.4, I substitute the ratio of resident 
patents to GDP in place of nonresident patents. Now there is 
no impact of technology. What really matters for growth in the 
full sample of countries seems to be technology transfer 
rather than creation. The latter is of greater importance in 
the advanced countries as we will see in the next section. In 
regression 4.5, I rescale patents by population. Nonresident 



77 
patents per capita continue to have a strong positive effect 
on per capita GDP growth. Again this suggests that for less 
industrialized countries imitation rather than creation of 
technology is their principal engine of growth. Finally, I 
incorporate all three variables in regression 4.6. These 
positive and significant results again suggest that in the 
world as a whole, the world-wide stock of disembodied 
technology and perhaps licensed technology are the main 
sources of growth. One striking result is that the coefficient 
of the rest of the world patents in the full sample compared 
with advanced countries is two times greater (0.111 vs 0.051) . 
This confirms in yet a different way that borrowing is more 
effective in all countries than in advanced countries. 

I now turn to impacts of the control variables. These are 
much the same as in chapter 3 except that enrollment ratios 
for primary and secondary levels are substituted for years of 
schooling to maintain sample size. 1 All the regressions in 
Table 4-4 support a concept of convergence — the negative 
relationship between initial GDP level and its subsequent 
growth. The human capital proxies have positive impacts on 
growth and are significant. Investment and government 
consumption ratios are positive and negative, respectively, 
and both are highly significant. In addition, the estimated 
coefficient of GDP and real oil price deviations, viewed as 
controls for business cycles, are positive and negative, 
respectively, and both are highly significant. Finally, and as 






78 
before, revolutions and coups, which measure political 
instability, have a negative impact. 
Results for Advanced Countries 

In advanced economies, due to their comparative 
advantage in conducting R&D, home based technology proxied by 
resident patents has a major impact on growth. At the same 
time the ratio of inventions to scientists and engineers 
raises the rate of growth. I also explore tentatively the 
potential for enhanced division of labor, through the 
interrelated growth of advanced and follower countries, and 
also the potential for world redistribution of output, which 
is potentially threatening to the advanced nations, as newly 
industrialized countries increase their technology 
acquisition. These redistributive effects could be a source of 
the widely noted economic slowdown in advanced countries. 

Tables 4-5 and 4-6 record the results for advanced 
countries. In general the data strongly support the greater 
role of technology creation in per capita GDP growth in these 
countries. Table 4-5 studies the effects of invention and 
inventive efficiency. In contrast, Table 4-6 reports 
experiments with the division of labor and world 
redistribution of output. 

In 5.1 I find that the effect of domestic patents per 
capita alone is positive but not significant. This might be 
explained by the idea of the economists who are persuaded that 
old lines of technological progress have been exhausted while 



79 
people are only slowly learning how to apply and exploit new 
potentials of, for example, computers and telecommunications. 
Also, there are large errors in the resident patent data. 
Regression 5.2 shows that the contribution of relative 
patenting efficiency to growth is both positive and 
significant. This result for technology efficiency agrees with 
Evenson's (1984) findings. Evenson reported that the ratio of 
patents granted per unit of inventive input had fallen from 
1964 to 1979-80 in most of the forty-four countries for which 
UNESCO data were available, and suggested that this fall 
contributed to a decrease in growth. 

Next I add the new technology efficiency variable to 
resident patents. Again the efficiency ratio is positive and 
significant but this is not the case for the resident patents. 
In regression 5.4, I add rest of the world resident patents to 
the other technology variables. The results indicate that all 
three have positive effects on growth. 

In regression 5.5 the significant and positive impacts of 
the home-based creative innovations and the spillover effects 
of the available stock of technology in the world have 
benefitted the advanced nations' growth. But the impact of 
these stocks in advanced countries is weaker than in less 
advanced ones. In regression 5.6 the results for nonresident 
patents are slightly weaker in advanced economies. The results 
in 5.5 and 5.6 are in line with the idea that within the 
subset of technological countries there is an ongoing process 



80 
of international productivity catch-up and technology 
convergence. In fact, the data show that even after 1973 the 
gaps between leader and follower countries continue to 
decline, although at a slower pace. It may be that the 
resources in trailing countries that, until recently were 
devoted mainly to borrowing and adapting existing advanced 
technology, are now being increasingly devoted to pushing the 
technological frontier itself. This implies that in the long 
run advances in knowledge benefit all countries. Thus advanced 
countries gain from the technological progress among 
themselves and from trade with more productive follower 
countries. Through the diffusion of knowledge and technology, 
the process of catch-up and convergence may be desirable not 
only for the countries catching up, but also for the old 
leader. 

I look next at the effect of the "standard" control 
variables: initial GDP, human capital variables, investment 
and government consumption ratios, GDP deviation, and oil 
price shocks in Table 4-5. The signs of these variables are 
mostly the same as in the full sample, though of course the 
level of significance is lower in this smaller sample. 2 In the 
case of primary and secondary enrollment ratios, we see no 
effect on growth except in regression 5.2. Not surprisingly, 
there is little effect of political instability on growth in 
advanced countries, since instability is rare. The business 
cycle variables and especially real oil price deviations are 






81 
more significant in advanced countries. This is because the 
industrialized countries' economies are more sensitive to oil 
price shocks. 

In Table 4-6, I record findings for some novel 
specifications involving proxies for the division of labor and 
world redistribution of output. I do this by looking at 
interrelated growth of advanced and follower countries perhaps 
for the first time. In this table I discuss only the 
coefficients related to the growth of incomes and technology 
variables since the behavior of the controls is similar to 
Table 4-5. 

In Table 4-6, I incorporate income growth and technology 
indicators for both NICs in general and Pacific Rim 3 countries 
into regressions for the advanced countries. In regression 6.1 
the estimated coefficient of average income growth of NICs — 
which has been weighted by GDP level of these countries—is 
positive and highly significant. Growth in the NICs is 
positively correlated with growth in advanced nations. A 
weaker version of the same result is obtained for the Pacific 
Rim countries. These results agree with Abramovitz (1990). He 
points to the important benefits and the possibility of 
positive externalities involved in the catch-up process. Then 
he explains that advanced countries like the United States can 
trade with more productive and efficient partners and get the 
benefits of cheap goods produced in those sectors when the 
trading partners' productivity has advanced rapidly. 



82 
Turning to the technology variables in the regressions 
6.3 through 6.6, the results show a negative impact of 
acquisition of technology in NICs and Pacific Rim countries on 
the growth of advanced countries. This result offers a 
potential explanation for the productivity slowdown in 
advanced countries. The point is that the potential for growth 
is weaker, but in the leading countries. Thus the newer 
industrializing countries of southern Europe, Southeast Asia, 
and Latin America can obtain a larger market share at the 
expense of lead countries. 
Results for Follower Countries 

Table 4-7 collects findings for follower countries which 
are useful in comparison with the analogous results in Table 
4-5. Estimated coefficients reported in Table 4-7 indicate 
that for follower nations only the stock of available 
technologies in the world and the transfer of technology from 
advanced countries are important. In other words, these 
results underscore the reliance in follower countries of 
transfer of expensive technology from advanced countries 
through imitation. Generally, these results agree with the 
findings of Pack and Westphal (1986) that "the minor role of 
invention in industrialization simply means that much 
technological change consists of assimilating and adapting 
foreign technology" (p. 105). 



83 
Interpretation of the Findings 
Table 4-8 collects the major results in the form of 
imputed effects, the products of means and regression 
coefficients. Group 1 reports the imputed effects from the 
full sample, where the source is regressions 4.1 - 4.3 and 
4.5. Group 2 records imputations from advanced countries, 
regressions 5.2, 5.5, 5.6, 6.1, and 6.4. Finally, group 3 
records imputed effects from follower countries, regressions 
7.2, 7.4-7.5. 

Group 1 reveals that the rest of the world's pool of new 
inventions strongly affects the growth in all countries (14%). 
The effects of nonresident patents as a ratio of GDP, 
nonresident, nonresident per capita, and resident patents are 
0.5%, 0.4%, 0.3%, and 0.2%, respectively. These results 
suggest that a major cause of growth across countries really 
is the spillover and externalities through access to the 
world-wide new inventions. In addition, technology transfer is 
the second major factor of growth. 

Group 2 summarizes the imputations from advanced 
countries. Again the rest of the world, s technology diffusion 
has the higher effect on growth among others (7%) . Average 
income growth in the NICs, nonresident patents, resident 
patents, and inventions per scientist and engineer effects on 
growth of advanced countries are 1.5%, 0.7%, 0.4%, and 0.4%, 
respectively. This suggests that again exchange of inventions 
is the major engine of growth among advanced nations. The 






84 
differences between group 1 and 2 lie in a higher effect of 
rest of the world's resident patents and on the effect of 
home-based technology which is almost zero in the full sample 
but positive in the advanced one. 

Group 3 reveals that once again the main source of 
technology effect on growth of follower countries is access to 
the pool of technology in the world (17%) . Nonresident patents 
and nonresident patents as a ratio of GDP effects are 0.74% 
and 0.71%, respectively. This supports the idea that imitation 
and adaptation of technology is the major source of follower 
countries' growth, especially in the NICs. 

Conclusion 
This chapter studies technology's contribution to growth. 
In advanced economies, consistent with their comparative 
advantage in R&D, domestic technology proxied by resident 
patents strongly influences growth. My results also suggest 
that imitation by less advanced countries of technologies 
developed in those more advanced nations is a main source of 
growth in these nations. I also find that in advanced 
countries, inventions per scientist and engineer raise the 
rates of growth, consistent with the literature on invention 
exhaustion. Finally, I find that the potential for enhanced 
division of labor, through interrelated growth of advanced and 
follower countries is associated with higher growth in the 
advanced countries, but that technology acquisition in the 






85 
newly industrializing countries is associated with 
deceleration of growth in the advanced countries. 

The results of this chapter also confirm income 
convergence, though conditioned on the other factors, such as 
imitating of technology transferred from leading countries by 
follower countries. The fact is that leading countries are 
paying the price for the catching up of less advanced 
countries. Through transferring technology these follower 
countries enjoy fast income growth. 

Finally, a possible interpretation of this chapter is 
that the great impact of the spillover effects from technology 
may justify government subsidies to R&D sectors in all 
countries. 



Notes 

1. Barro (1991) has used the same proxies for human capital. 
The reason I used these variables instead of my own variable 
for human capital as in chapter 3 is that by using mv 
variables have fewer observations. 

2. One exception is government spending. In the advanced 
countries this has positive sign but is insignificant, 
suggesting that probably there is a zero impact of government 
spending on growth in advanced countries and it is in contrary 
with was the negative effect of government spending in full 
sample. It might be that there are better government policies 
in the advanced countries. 

3. Pacific Rim countries include Japan, Korea, Philippines, 
and Singapore. *^ ' 



86 



Table 4-4 

Regressions for per Capita GDP Growth in the Full Sample 

Experiments with the Effect of Technology Creation and Transfer 

(T-statistics in Parentheses) 



VARIABLE 


(4.1) 

0.013 
(0.41) 


(4.2) 


(4.3) 


(4.4) 


(4.5) 


(4.6) 


CONSTANT 


0.057 
(2.06) 


0.172 
(6.55) 


0.142 
(5.84) 


0.171 
(6.31) 


0.034 
(1.00) 


LOG(GDP60) 
ENROLLMENT RATIO: 


-0.023 
(-6.86) 


-0.027 
(-7.63) 


-0.026 
(-6.96) 


-0.022 
(-6.32) 


-0.026 
(-6.71) 


-0.026 
(-7.17) 


PRIMARY 


0.024 
(2.31) 


0.019 
(1.87) 


0.023 
(2.13) 


0.025 
(2.30) 


0.024 
(2.26) 


0.021 
(1.99) 


SECONDARY 
GDP SHARE OF: 


0.041 
(3.09) 


0.047 
(3.77) 


0.027 
(2.31) 


0.024 
(2.01) 


0.026 
(2.17) 


0.042 
(3.15) 


INVESTMENT 
GOVERNMENT 


0.175 
(7.27) 


0.183 
(7.58) 


0.179 
(7.16) 


0.196 
(7.57) 


0.184 
(7.40) 


0.181 
(7.48) 


SPENDING 


-0.131 
(-4.99) 


-0.133 
(-5.13) 


-0.137 
(-5.15) 


-0.135 
(-4.90) 


-0.131 
(-4.94) 


-0.129 
(-4.93) 


GDP DEVIATION 


0.420 
(4.01) 


0.413 
(3.95) 


0.409 
(3.80) 


0.387 
(3.56) 


0.397 
(3.68) 


0.417 
(3.99) 



REVOL. & COUPS -0.008 -0.008 -0.010 -0.010 -0.010 -0.008 

(-2.25) (-2.18) (-2.44) (-2.61) (-2.44) (-2.14) 

REAL OIL PRICE 

DEVIATION -0.017 -0.017 -0.016 -0.016 -0.016 -0.017 

(-2.88) (-2.87) (-2.54) (-2.57) (-2.55) (-2.90) 

INTERNATIONAL 
AND CIVIL WAR 

YEAR (DUMMY) 0.012 0.015 0.016 0.016 0.016 0.013 

(1.86) (2.21) (2.41) (2.36) (2.39) (1.98) 

REST OF THE WORLD 

RESIDENT PATENTS 0.111 0.094 - - - - _ _ 107 

(6.10) (6.56) (5 ; 89) 

RESIDENT PATENTS 0.062 - - n n0/r 

(2 20} 0.036 

U *^ U > (1.16) 

NONRESIDENT PATENTS - - 0.086 - - - - _ _ 0>069 



(2.77) (2.04) 



87 
Table 4-4 — continued 



VARIABLE (4.1) 


(4.2) 


(4.3) 


(4.4) 


(4.5) 


(4.6) 


RATIO OF 
NONRES. PATENTS/ GDP - - 


- - 


0.122 
(2.79) 








RATIO OF 






-0.113 
(-0.75) 


- - 


- - 


NONRESIDENT PATENTS 








0.012 
(2.27) 


- - 


F 18.18 


18.52 


15.77 


14.87 


15.44 


17.10 


R 2 0.24 


0.24 


0.19 


0.18 


0.19 


0.24 


NOB 611 


611 


611 


611 


611 


611 



Note: The full sample is all the countries in Table 4-1. 



88 



Table 4-5 
Regressions for per Capita GDP Growth in the Advanced Countries 

Experiments with the Effects of 
Invention and Inventive Efficiency 
(T-statistics in Parentheses) 



VARIABLE 



(5.1) (5.2) (5.3) (5.4) (5.5) (5.6) 



CONSTANT 
LOG(GDP60) 



ENROLLMENT RATIO: 
PRIMARY 



SECONDARY 



GDP SHARE OF: 
INVESTMENT 

GOVERNMENT 
SPENDING 



GDP DEVIATION 



REAL OIL PRICE 
DEVIATION 



REVOL. & COUPS 



REST OF THE WORLD 
RESIDENT PATENTS 



RESIDENT PATENTS 



NONRESIDENT 
PATENTS 



0.164 
(2.32) 



0.130 
(2.05) 



0.142 
(1.91) 



0.075 
(0.99) 



0.089 
(1.18) 



0.066 
(0.90) 



-0.023 -0.020 -0.021 -0.020 -0.021 -0.019 
(-2.82) (-2.78) (-2.65) (-2.57) (-2.72) (-2.54) 



0.010 
(0.87) 

0.019 
(1.65) 

0.133 
(4.47) 

0.026 
(0.73) 

0.371 
(4.05) 



0.012 
(1.02) 

0.025 
(2.15) 



0.133 
(4.51) 

0.027 
(0.76) 

0.367 
(4.05) 



0.009 
(0.68) 

0.021 
(1.42) 



0.136 
(4.35) 

0.029 
(0.80) 

0.367 
(4.04) 



0.012 
(0.91) 

0.020 
(1.38) 

0.110 
(3.46) 

0.028 
(0.77) 

0.379 
(4.24) 



0.009 
(0.66) 

0.010 
(0.80) 



0.113 
(3.54) 

0.028 
(0.79) 

0.380 
(4.24) 



0.016 
(1.24) 

0.023 
(1.97) 



0.147 
(4.36) 

0.041 
(1.14) 

0.372 
(4.19) 



-0.018 -0.017 -0.017 -0.017 -0.017 -0.018 

(-3.38) (-3.34) (-3.35) (-3.39) (-3.43) (-3.53) 

-0.001 -0.0004 -0.0004 -0.001 -0.001 -0.004 

(-0.15) (-0.06) (-0.06) (-0.22) (-0.27) (-0.67) 



0.007 
(0.33) 



0.051 
(3.11) 

0.050 
(1.97) 



0.056 
(3.52) 

0.065 
(2.75) 



0.035 
(2.58) 



0.064 
(3.59) 



89 



Table 4-5 — continued 



VARIABLE 



(5.1) (5.2) (5.3) (5.4) (5.5) (5.6 



RATIO OF RESIDENT 














PATENTS / S&E 


"" 


0.034 
(2.41) 


0.033 
(2.16) 


0.022 
(1.45) 


— — 


— — 


RESIDENT PATENTS 














PER CAPITA 


0.013 
(0.97) 












F 


5.81 


6.46 


5.80 


6.33 


6.73 


7.38 


R 2 


0.14 


0.15 


0.15 


0.18 


0.17 


0.19 


NOB 


269 


269 


269 


269 


269 


269 



Note: See Table 4-1 for the definition of the advanced countries; 
basically these are high income western countries. 






90 

Table 4-6 

Regressions for per Capita GDP Growth in the Advanced Countries 
Experiments with Division of Labor and World 
Redistribution of Output through Inter-related 
Growth of Advanced and Follower Countries 
(T-statistics in Parentheses) 



VARIABLE 



(6.1) (6.2) (6.3) (6.4) (6.5) 



(6.6) 



AVERAGE INCOME 
GROWTH OF NICs 
COUNTRIES WEIGHTED 
BY GDP SHARE 6.810 

(7.48) 



AVERAGE INCOME 
GROWTH OF PACIFIC 
RIM COUNTRIES 
WEIGHTED BY GDP 
SHARE 



2.800 
(4.83) 



AVERAGE INCOME 
GROWTH OF NON-PACIFIC 
RIM COUNTRIES OF 
NICs SAMPLE WEIGHTED 
BY GDP SHARE 



0.566 
(1.03) 



RATIO OF RESIDENT 
PATENTS/S&E 



0.025 
(1.74) 



0.029 
(2.08) 



RATIO OF PATENT/S&E - - 



AVERAGE PATENT/S&E 
OF NICs COUNTRIES 
WEIGHTED BY 
GDP SHARE 

AVERAGE RESIDENT 
PATENTS/S&E 
OF NICs COUNTRIES 
WEIGHTED BY 
GDP SHARE 



0.005 
(2.06) 



-0.0001 
(-3.52) 



-0.0008 
(-2.39) 




(3, 



009 
33) 



91 
Table 4-6 — continued 



VARIABLE 



(6.1) (6.2) (6.3) (6.4) (6.5) (6.6) 



AVERAGE PATENT/S&E 
OF PACIFIC RIM 
COUNTRIES WEIGHTED 

BY GDP SHARE -- -- -0.200 

(-0.64) 



AVERAGE RESIDENT 
PATENTS/ S&E 
OF PACIFIC RIM 
COUNTRIES WEIGHTED 










-2.151 
(-4.25) 


F 


14.80 


13.49 


8.85 


7.24 


7.19 


8.91 


R 2 


0.29 


0.29 


0.21 


0.17 


0.17 


0.21 


NOB 


269 


269 


269 


269 


269 


269 



92 



Table 4-7 
Regressions for per Capita GDP Growth in the Follower Countries 
Experiments with the Effect of Technology Transfer 
(T-statistics in Parentheses) 



VARIABLE 



CONSTANT 



LOG(GDP60) 



(7.1) (7.2) (7.3) (7.4) 



(7.5) 



0.011 0.019 0.164 0.199 0.230 

(0.26) (0.48) (4.77) (5.38) (5.77) 

-0.027 -0.029 -0.026 -0.030 -0.033 

(-5.72) (-5.88) (-5.17) (-5.71) (-6.09) 



ENROLLMENT RATIO: 
PRIMARY 



SECONDARY 



0.011 
(0.73) 

0.061 
(2.14) 



0.012 
(1.87) 

0.058 
(2.64) 



0.026 
(1.70) 

0.016 
(0.54) 



0.021 
(1.41) 



0.020 
(1.37) 



0.003 -0.011 
(0.18) (-0.53) 



GDP SHARE OF: 
INVESTMENT 

GOVERNMENT 
SPENDING 



GDP DEVIATION 



0.224 
(6.83) 



0.220 
(6.84) 



0.224 
(6.43) 



0.203 
(6.00) 



0.217 
(6.52) 



-0.166 -0.167 -0.145 -0.171 -0.187 

(-5.03) (-5.47) (-4.30) (-5.01) (-5.37) 

0.380 0.372 0.337 0.368 0.340 

(2.63) (2.58) (2.24) (2.45) (2.28) 



REAL OIL PRICE 
DEVIATION 

INTERNATIONAL 
AND CIVIL WAR 
YEAR (DUMMY) 



REST OF THE WORLD 
RESIDENT PATENTS 



RESIDENT PATENTS 



-0.020 -0.020 -0.017 -0.016 -0.016 
(-2.42) (-2.38) (-1.97) (-1.92) (-1.88) 



0.010 
(1.16) 



0.136 

(5.47) 

-0.008 
(-0.14) 



0.009 
(1.07) 



0.138 
(6.23) 




(1 



017 
79) 



0.018 
(1.94) 



0.019 
(2.03) 



RATIO OF RESIDENT 
PATENTS / S&E 



continued next page 



-0.00002 
(-1.24) 



93 
Table 4-7 — continued 



VARIABLE 


(7.1) 


(7.2) (7.3) 


(7.4) 


(7.5) 




RATIO OF 
NONRES . PATENTS/GDP 






0.311 
(2.56) 


- - 




RATIO OF 
RESID. PATENTS/GDP 


- - 


- - -0.255 
(-0.70) 


- - 


- - 




NONRESIDENT PATENTS 
PER CAPITA 








) nnnno 





(3.24) 



F 

"j2 



14.92 15.14 11.29 12.12 12.67 
R ' 0-25 0.24 0.18 0.19 0.20 

N0B 413 413 413 413 413 



Note: The follower countries are all the countries except the 
advanced countries. See the notes to Table 4-1. 



94 



Table 4-8 

Estimated Mean Contributions to Economic Growth 

(T-statistics in Parentheses) 



VARIABLE 



FULL 
SAMPLE 



ADVANCED 
COUNTRIES 



FOLLOWER 
COUNTRIES 



REST OF THE WORLD 
RESIDENT PATENTS 



RESIDENT PATENTS 



NONRESIDENT PATENTS 



RATIO OF NONRESIDENT 
PATENTS/GDP 



NONRESIDENT PATENTS 
PER CAPITA 



0. 


136 


(6 


.10) 


0. 


0019 


(2 


.20) 


0. 


0039 


(2 


.77) 


0. 


0046 


(2 


.79) 


0. 


0029 


(2 


.27) 



0.067 
(3.52) 



0.0036 
(2.75) 



0.0069 
(3.59) 



0.170 
(6.23) 



0.0071 
(2.56) 



0.0074 
(3.24) 



RATIO OF RESIDENT 
PATENTS/ S&E 



0.0036 
(2.41) 



AVERAGE INCOME 
GROWTH OF NICs 
COUNTRIES WEIGHTED 
BY GDP SHARE 



0.0149 
(7.48) 



AVERAGE RESIDENT 
PATENTS/S&E 
OF NICs COUNTRIES 
WEIGHTED BY 
GDP SHARE 



-0.0068 
(-2.39) 



NOTE: Estimated contributions are regression coefficients times 
means of variables. 



CHAPTER 5 
SUMMARY AND CONCLUSIONS 
The goal of this dissertation has been to explore the 
causes of differences in growth performance across countries, 
guided by the new theories of endogenous economic growth. 
Chapter 2 tests the impact of oil price shocks in advanced 
countries. Chapter 3 studies the effects of human capital, 
both its initial level and its rate of growth. Chapter 4 
investigates the effect of technology, both in its creation 
and in its transfer, on the growth of advanced and developing 
nations. 

Chapter 2 focuses on government policy toward the 
transportation sector. The interaction between oil price 
changes and the share of petroleum-based transportation in ten 
OECD countries is studied. It has been shown that fluctuations 
in oil-prices have trend as well as more conventional cyclical 
effects on the patterns of economic growth in advanced 
nations. The analysis of data for ten OECD countries 
demonstrates, for example, that the relative share of a 
country's transportation sector in its total consumption of 
oil has a major impact on that nation's petroleum dependency. 
The larger the transportation sector's share, the more 
sensitive to oil-shocks a nation's economy is. More important, 

95 



96 
however, the transportation sector's price elasticity with 
respect to oil is an important determinant of a nation's 
economic growth. Basically, if petroleum cannot easily be 
replaced in the transportation sector, then a country finds 
itself at the mercy of oil price shocks. Comparisons between 
the United States and countries with more flexible energy 
strategies, such as Japan, demonstrate a growth advantage in 
the more flexible countries. 

Chapter 3 tests the effect of human capital on growth. It 
considers how initial levels of human capital and growth of 
human capital as well as how government consumption, 
investment, political instability, initial level of GDP, and 
public infrastructure influence economic growth. From the 
empirical results, it appears that nations with higher initial 
incomes experience lower rates of growth than do initially 
lower income countries. This suggests that countries' GDPs may 
eventually converge (Barro, 1991). Also in line with 
theoretical works by Lucas (1988) and Romer (1990), it was 
found that the initial stock of human capital and its 
subsequent growth have major impacts on national growth 
perhaps through spill-over effects. Strikingly, the findings 
show that the higher the initial stock of human capital and 
the faster its growth, the larger the rate of growth of per 
capita income. In other words, countries that invest heavily 
in education grow faster, and initial stocks of education 
confer a growth advantage, in addition, the combination of 






97 
results in chapters 3 and 4 shows that human capital is 
central to the adoption of new technologies. Therefore, the 
associate for growth rates across nations to be positively 
correlated with initial human capital may reflect the enhanced 
capacity for using the technological advances that have been 
developed in other countries. 

In line with recent models, the findings in chapter 3 

show that expansion in government consumption, political 

instability, and increased rates of population are associated 

with lower rates of growth. On the other side of the ledger, 

it was shown that investments in infrastructure accelerate 

growth. Several studies overlap with and agree with these 

results. Barro (1990) confirms the negative effect of 

government consumption on growth. In theoretical work, Becker 

and Murphy (1990) have suggested that population growth and 

income growth can be inversely related. Also, Aschauer (1989) 

has studied the positive impact of U.S. public infrastructure 

on U.S. growth and has found a positive effect. 

Chapter 4 studies technology's contribution to growth. In 
advanced economies, consistent with their comparative 
advantage in R&D, domestic technology proxied by resident 
patents strongly influences growth. However, the results 
suggest that imitation by less advanced countries of 
technologies developed in those more advanced is the principal 
engine of growth in these nations. Therefore, for most 
countries, the important consideration is not discoveries of 



98 
new inventions, but rather the rate of absorption of the new 
technologies that have been introduced by the advanced 
countries. The advantage of being a follower — not having to 
invest in basic research — provides another force toward 
convergence across countries. That is, the poorer countries 
can grow more rapidly than advanced ones because it is cheaper 
to adapt advanced technologies than to make their own 
technologies. It has also found that in advanced countries, 
inventions per scientist and engineer raise rates of growth, 
which is consistent with the literature on invention 
exhaustion. Finally, the results suggest that the potential 
for enhanced division of labor, through the interrelated 
growth of advanced and follower countries is tentative because 
likely associated with higher growth in the advanced 
countries, but that technology acquisition in the newly 
industrializing countries is associated with deceleration of 
growth in the advanced countries through redistribution of 
world output. 

There are several policy implications flowing from the 
results in this dissertation. In the case of oil import 
dependency in advanced economies, the government should 
encourage fuel substitutability . 

Second, because of the strong impact of human capital on 
growth, in so far as there are spillovers, governments should 
increase their investment in education and training skilled 
people. From the results in chapter 3, it seems that this 



99 
investment has a high return and pays rich dividends, though 
the case for educational spillovers has not yet been 
rigorously demonstrated. 

Third, because of the contribution of technology to 
growth through spillover effects, the results in chapter 4 
suggest an activist government policy toward technology 
acquisition and a profitable climate for technology licensing. 

Finally, this dissertation has studied only some factors 
affecting growth across countries. More work needs to be done 
on human capital spillovers and on the interaction between 
human capital and technology transfer. Finally, the effect of 
war on economic growth and the origins of this effect are 
essentially unexplored territory in growth economics. 






APPENDIX A 
DEFINITION OF VARIABLES IN CHAPTER 2 
Definition of variables and source of data for chapter 2 
are: 

DOP = change in real oil price, using world price of "Arabian 

Light", then multiplying and dividing by each country's 

exchange rate and price index, respectively (International 

Financial Statistics / IMF) . 

DRM = growth in money base, using Reserve Money data 

(International Financial Statistics / IMF) . 

DGC = growth in Government Consumption (International 

Financial Statistics / IMF) . 

DGNP = growth in real GNP/GDP (International Financial 

Statistics / IMF) . 

DTINTER = interaction of real oil price and share of 

transport's oil consumption (ROP x SHARE) changes (Energy 

Balances of OECD Countries) . 

MINTER = interaction between DOP and mean share of transport's 
consumption of oil (Energy Balances of OECD Countries) . 
DTIN = change in industry consumption of oil (Energy Balances 
of OECD Countries) . 

DTTR = change in transportation consumption of oil (Energy 
Balances of OECD Countries) . 



100 



101 
DOP1 = the first lagged DOP. 
DOP2 = the second lagged DOP. 
DOP3 = the third lagged DOP. 

ROP = real oil price (International Financial Statistics / 
IMF) . 

DTIS = the difference between growth of transportation and 
industry of oil consumption (DTTR - DTIN) . 



APPENDIX B 

DEFINITIONS AND METHOD OF CALCULATION OF 
MEAN YEARS OF SCHOOLING 

Definitions 

Tables of statistics in UNESCO statistical yearbooks 

provide the percentage distribution of the highest level of 

educational attainment of the adult population. The data are 

derived from national censuses or sample surveys and were 

provided by the United Nations Statistical Office or were 

derived from regional or national publications. The category 

of different levels of education in this source are: 

a) No Schooling: This term applies to those who have completed 
less than one tear of schooling. 

b) Incomplete Primary Level: This category includes all those 
who completed at least one year of education at the primary 
level but who did not complete the final grade at this level. 
The duration of education at the primary level may vary 
depending on the country. 

c) Completed Primary Level: Those who complete the final grade 
of education at the primary level but did not go on to 
secondary level studies are included in this group. 

d) Entered Secondary Level, First Stage: This group comprises 
those whose level of educational attainment was limited to the 
lower stage of education at the secondary level. 

102 






103 

e) Entered Secondary Level, Second Stage: This group consists 
of those who moved to the higher stage of secondary level 
education from the lower stage, but did not proceed to studies 
at the third level. 

f) Post Secondary: Anyone who undertook third level studies, 
whether or not they completed the full course, are counted in 
this group. 

The number of persons whose level of education was not 
stated has been subtracted from the total population. 
Calculation of the Educational Attainment: 

Other than UNESCO Statistical Year Books (1963-89) , from 
which data for educational attainment have been available, I 
have calculated educational attainment for some countries 
using Demographic Statistical Year Books (various years) and 
Kaneko (1986). Below I describe my method of calculating 
educational attainment for these countries so that the 
measures are similar to the UNESCO data: 

a) divide the number of population with no schooling into 
the total population. 

b) add the number of persons with a primary level completion 

(except the last year of primary school) . Then divide by 
total population (incomplete primary) . 

c) divide the number of population who completed primary 
school into the total population. 

d) add those who have 2 or 3 years of the first stage of 
secondary school and then divide into total population. 



104 

e) add those with two or three years of the second stage 
of secondary school and divide into total population. 

f) finally add the persons with post secondary and 
university level and divide into total population. 

For the calculation of mean years of schooling for some 
countries, I have used information on education of older or 
younger cohorts. For example, the average level of education 
of the population aged 3 5+ in 1970 were roughly equivalent to 
those aged 25+ in 1960. For most OECD countries I simply 
assumed that everyone had completed the full primary cycle, 
which was obviously not correct for the entire population and 
for all those countries. Therefore, I had to make somewhat 
rough assumptions for primary education level in some years. 
Method of Calcula tion of Mean Years of Schooling 

In order to estimate the mean number of years of 
schooling of total population in each country, the following 
formula, adopted from Psacharopoulos and Arriagada (1986) , was 
used: 

S = [LP1 * (YRSP/2) + (LP2 * YRSP) + LSI * ( (YRSP + (YRSS/2)) 
+ LS2 * (YRSP + YRSS) + LH * (YRSP + YRSS + YRSH) ] / 100 
where : 

S = mean number of years of schooling 

LP1 = percentage of the population with incomplete primary 

schooling 
YRSP = number of years of primary level education cycle 
LP2 = schooli^ 6 ° f thG population with completing primary 



105 
LSI = percentage of the population with incomplete secondary 

schooling 
YRSS = number of years of secondary level education cycle 
LS2 = percentage of the population with complete secondary 

schooling 
LH = percentage of the population with complete and 

incomplete higher level education 
YRSH = number of years of higher level education cycle 






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BIOGRAPHICAL SKETCH 
Hamid-Reza Baradaran-Shoraka was born in Torbat 
Heidarieh, Iran, in 1953. He earned a Bachelor of Arts degree 
from the University of Allameh Tabatabai, in Iran, in 1975, 
and a Master of Arts from the University of Florida in 1989. 
He was hired by the University of Allameh Tabatabai in 1975 as 
an instructor and has been a faculty member of the Economics 
Department since that time. 






110 



I certify that I have read this study and that in my 
opinion it conforms to acceptable standards of scholarly 
presentation and is fully adequate, in scope and quality, as 
a dissertation for the deqree of Doctor of Philosophy. 



Jiames D. Adams, Chair 
Professor of Economics 



I certify that I have read this study and that in my 
opinion it conforms to acceptable standards of scholarly 
presentation and is fully adequate, in scope and quality, as 
a dissertation for the degree of Doctor of Philosophy. 



i 



/ ; L_A I :^_x 



Mark Rush 

Professor of Economics 



^ 



I certify that I have read this study and that in my 
opinion it conforms to acceptable standards of scholarly 
presentation and is fully adequate, in scope and quality, as 
a dissertation for the degree of Doctor of Philosophy. 

David Denslow 
Professor of Economics 

I certify that I have read this study and that in my 
opinion it conforms to acceptable standards of scholarly 
presentation and is fully adequate, in scope and quality, as 
a dissertation for the degree of Doctor of Philosophy. 

Prakash Loungani 

Assistant Professor of Economics 

I certify that I have read this study and that in my 
opinion it conforms to acceptable standards of scholarly 
presentation and is fully adequate, in scope and guality, as 
a dissertation for the degree of Doctor of Philosophy. 



F 



James L. Seale 
Associate Professor of Food 
and Resource Economics 



This dissertation was submitted to the Graduate Faculty 
of Department of Economics in the College of Business 
Administration and to the Graduate School and was accepted as 
partial fulfillment of the reguirements for the degree of 
Doctor of Philosophy. 



August, 1992 



Dean, Graduate School 



UNIVERSITY OF FLORIDA 



3 1262 08555 3666